Main
Dmcourse.Main History
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[l] Attach:chap8.pdf, Attach:chap10.pdf
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[l] Attach:chap8.pdf, Attach:chap9.pdf
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[l] FPM: Sequence Mining
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[l] FPM: Sequence Mining
[l] Attach:chap10.pdf
[l] Attach:Lecture21.PDF
[l] Attach:chap10.pdf
[l] Attach:Lecture21.PDF
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[l] CLUS: Spectral & Graph Clustering
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[l] CLUS: Evaluation & Assessment
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[l] Attach:Lecture18.PDF
[l] Attach:Lecture18.PDF
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] CLUS: Evaluation & Assessment
[l] Attach:chap18.pdf
[l] Attach:chap18.pdf
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[l] FPM: Sequence Mining
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] FPM: Graph Mining
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[l] FPM: Sequence Mining
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[l] FPM: Pattern Assessment
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[l] FPM: Graph Mining
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[l] Attach:chap19.pdf
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[l] Attach:chap18.pdf
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[l] Attach:Lecture17.PDF
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[l] Attach:chap19.pdf
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* Nov 8: [[Assign5 | Assign5]] has be posted. It is due on 16th Nov, before midnight.
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[l] Attach:chap15.pdf, Attach:chap15.pdf
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[l] Attach:chap14.pdf, Attach:chap15.pdf
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[l] CLUS: EM-based
[l]
[l] Attach:Lecture15.PDF
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[l]R: Nov 8
[l]
[l] Attach:Lecture15.PDF
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[l]R: Nov 8
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[l] CLUS
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[l] Attach:chap15.pdf, Attach:chap15.pdf
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[l] Clustering (CLUS): Hierarchical, Partitional
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[l] Clustering (CLUS): Partitional
[l] Attach:chap13.pdf
[l] Attach:chap13.pdf
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[l] CLUS: Density-based Clustering
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[l] CLUS: Hierarchical, Density-based Clustering
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* Oct 23: [[Assign4 | Assign4]] has be posted. It is due on 30th Oct, before midnight.
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[l] CLASS: Classifier Evaluation
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[l] CLASS: Classifier Evaluation
[l] Attach:Lecture13.PDF
[l] Attach:Lecture13.PDF
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[l] Attach:chap21.pdf, Attach:chap19.pdf
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* Oct 12: [[Assign3 | Assign3]] has be posted. It is due on 19th Oct, before midnight.
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[l] Attach:chap22.pdf, Attach:chap23.pdf
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[l] Attach:chap22.pdf
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[l] Attach:chap23.pdf
[l] Attach:Lecture10.PDF
[l] Attach:Lecture10.PDF
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[l] Attach:Lecture9.PDF
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[l] CLASS: SVMs, Bayesian Classifier
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[l] CLASS: SVMs
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[l] ASS: Decision Trees
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[l] CLASS: Bayesian Classifier, Decision Trees
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[l] Attach:chap22.pdf
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[l] Attach:chap22.pdf, Attach:chap23.pdf
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[l] DA: Kernels, Classification (CLASS): Linear Discriminants
[l]Attach:chap22.pdf
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[l]
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[l] DA: Kernels
[l]
[l] Attach:Lecture8.PDF
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[l] Attach:Lecture8.PDF
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[l] CLASS: SVMs
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[l] Classification (CLASS): Linear Discriminants, SVMs
[l] Attach:chap22.pdf
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[l] Attach:chap22.pdf
[l]
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[l] Classification (CLASS): Linear Discriminants & SVMs
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[l] DA: Kernels, Classification (CLASS): Linear Discriminants
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[l] CLASS: Bayesian Classifier CL
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[l] CLASS: SVMs, Bayesian Classifier
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[l] Classification (CLASS): Bayesian Classifier
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[l] Classification (CLASS): Linear Discriminants & SVMs
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[l] CLASS: Decision Trees
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[l] CLASS: SVMs
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[l] CLASS: Linear Discriminants & SVMs
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[l] CLASS: Bayesian Classifier CL
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[l] CLASS: SVMs
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[l] ASS: Decision Trees
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* Sep 24: [[Assign2 | Assign2]] has be posted. It is due on 1st Oct, before midnight.
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[l] DA: Categorical Data & Kernel Methods
[l] Attach:chap3.pdf, Attach:chap5.pdf
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[l] DA: Categorical Data &
[l] Attach:chap3.pdf
[l] Attach:Lecture6.PDF
[l] Attach:chap3.pdf
[l] Attach:Lecture6.PDF
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[l] DA: Dimensionality Reduction
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[l] DA: Kernel Methods
[l] Attach:chap5.pdf
[l] Attach:chap5.pdf
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[l] DA: Categorical Data & High Dimensional Analysis
[l] Attach:chap3.pdf, Attach:chap6.pdf
[l] Attach:
to:
[l] DA: High Dimensional Analysis
[l] Attach:chap6.pdf
[l] Attach:Lecture5.PDF
[l] Attach:chap6.pdf
[l] Attach:Lecture5.PDF
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[l] Kernel Methods
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[l] DA: Categorical Data & Kernel Methods
[l] Attach:chap3.pdf, Attach:chap5.pdf
[l] Attach:chap3.pdf, Attach:chap5.pdf
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[l] DA: Dimensionality Reduction & Categorical Data
[l] Attach:chap3.pdf , Attach:chap7.pdf
[l] Attach:chap3.pdf ,
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[l] DA: Dimensionality Reduction
[l] Attach:chap7.pdf
[l] Attach:chap7.pdf
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[l] DA: High Dimensional Analysis
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[l] DA: Categorical Data & High Dimensional Analysis
[l] Attach:chap3.pdf, Attach:chap6.pdf
[l] Attach:chap3.pdf, Attach:chap6.pdf
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[l] Attach:chap3.pdf
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[l] Attach:chap3.pdf , Attach:chap7.pdf
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* Sep 14: [Dmcourse/Assign1 | Assign1]] has be posted. It is due on 21st Sep, before midnight.
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* Sep 14: [[Assign1 | Assign1]] has be posted. It is due on 21st Sep, before midnight.
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* Sep 14: [Dmcourse/Assign1 | Assign1]] has be posted. It is due on 21st Sep, before midnight.
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[l] DA: Numeric and Categorical Attributes
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[l] DA: Numeric Attributes
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[l] DA: Numeric and Categorical Attributes
[l] Attach:chap3.pdf
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[l] DA: Numeric Attributes: Eigen-decomposition
[l]
[l]
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[l] DA: Kernel Approach and Graph Analysis
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[l] DA: Dimensionality Reduction & Categorical Data
[l] Attach:chap3.pdf
[l] Attach:chap3.pdf
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[l] DA: Dimensionality Reduction
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[l] Kernel Methods
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[l] Attach:chap1.pdf, Attach:chap2.pdf
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[l] Attach:chap1.pdf
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[l] Attach:chap3.pdf
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[l] Attach:chap2.pdf
[l] Attach:Lecture2.PDF
[l] Attach:Lecture2.PDF
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[l] DA: Kernel Approach
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[l] DA: Numeric and Categorical Attributes
[l] Attach:chap3.pdf
[l] Attach:chap3.pdf
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[l] DA: Graph Analysis
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[l] DA: Kernel Approach and Graph Analysis
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* Sep 7: Everyone enrolled in the course should have already signed up for the piazza account (or they should have received an email to do so). Please sign up immediately to receive class announcements and emails.
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[!c]Lectures
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[l] Attach:dmintro.pptx, Attach:Lecture1.PDF
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[l] (Attach:)chap1.pdf, (Attach:)chap2.pdf
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[l] Attach:chap1.pdf, Attach:chap2.pdf
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[l] (Attach:)chap3.pdf
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[l] Attach:chap3.pdf
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[l] (Attach:)chap1.pdf, (Attach:)chap2.pdf
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[l] (Attach:)chap3.pdf
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[l] CLASS: SVMs FPM: Pattern Assessment
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[l] CLASS: SVMs
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[l] CLUS: Spectral & Graph Clustering
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[l] CLUS: Evaluation & Assessment
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[l] CLUS: Evaluation & Assessment
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] FPM: Sequence Mining
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[l] FPM: Sequence Mining
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[l] FPM: Graph Mining
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[l] FPM: Graph Mining
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[l] FPM: Pattern Assessment
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[l] DA: Numeric Attributes
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[l] DA: Numeric and Categorical Attributes
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[l] DA: Categorical Attributes
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[l] DA: Kernel Approach
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[l] DA: Kernel Approach
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[l] DA: Graph Analysis
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[l] DA: High Dimensional Analysis
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[l] DA: High Dimensional Analysis
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[l] DA: Dimensionality Reduction
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[l] DA: Dimensionality Reduction
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] DA: Dimensionality Reduction
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[l] FPM: Sequence Mining
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[l] Classification (CLASS): Bayesian Classifier
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[l] DA: Graph Analysis
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[l] CLASS: Decision Trees
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[l] DA: Graph Analysis & Mining
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[l] CLASS: Linear Discriminants & SVMs
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[l] FPM: Graph Mining
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[l] CLASS: SVMs FPM: Pattern Assessment
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[l] FPM: Pattern Assessment
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[l] CLASS: Classifier Evaluation
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[l] Classification (CLASS): Bayesian Classifier
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[l] CLASS: Classifier Evaluation
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[l] CLASS: Decision Trees
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[l] Clustering (CLUS): Hierarchical, Partitional
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[l] CLASS: Linear Discriminants & SVMs
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[l] CLUS: Density-based Clustering
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[l] CLASS: SVMs
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[l] CLUS: Subspace Clustering
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[l] CLASS: Classifier Evaluation
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[l] CLUS: Spectral & Graph Clustering
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[l] Clustering (CLUS): Hierarchical, Partitional
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[l] CLUS: Spectral & Graph Clustering
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[l] CLUS: Density-based Clustering
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[l] CLUS: Evaluation & Assessment
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[l] CLUS: Spectral & Graph Clustering
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] CLUS: Subspace Clustering
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[l] FPM: Sequence Mining
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[l] CLUS: Evaluation & Assessment
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[l] FPM: Graph Mining
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'''TA Office Hours''': TBA\\
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'''TA Office Hours''': W 2-4PM, Amos Eaton 119\\
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'''TA Contact''': [[hidden-email:gnyxha@ecv.rqh]]
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'''TA Contact''': [[hidden-email:gnyhxa@ecv.rqh]]
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'''TA Contact''': email:talkun@rpi.edu
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'''TA Contact''': [[hidden-email:gnyxha@ecv.rqh]]
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'''TA Contact''': emailto:talkun@rpi.edu
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'''TA Contact''': email:talkun@rpi.edu
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'''TA Contact''': [[email:talkun@rpi.edu]]
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'''TA Contact''': emailto:talkun@rpi.edu
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'''TA Contact''': [[ehidden-email:gnyhxa@ecv.rqh]]
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'''TA Contact''': [[email:talkun@rpi.edu]]
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'''Room''': TBA\\
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'''Room''': Greene 120\\
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'''TA''': TBA\\
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'''TA''': Nilothpal Talukder\\
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'''TA Contact''': TBA
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'''TA Contact''': [[ehidden-email:gnyhxa@ecv.rqh]]
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[l] DA: Numeric Attributes
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[l]DA: Numeric Attributes
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[l] DA: Categorical Attributes
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[l]%blue%Thanksgiving Break%%
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[l]%red%Thanksgiving Break%%
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'''Instructor Office Hours''': 12-1PM, MR, Lally 307\\
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'''Instructor Office Hours''': MR 12-1PM, Lally 307\\
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Students will be given draft chapters from the forthcoming book
* Data Mining and Analysis: Foundations and Algorithms, Mohammed J. Zaki and Wagner Meira, Jr, Cambridge University Press, 2013.
* Data Mining and Analysis: Foundations and Algorithms, Mohammed J. Zaki and Wagner Meira, Jr, Cambridge University Press, 2013.
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* Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible for brushing up on any missed material.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, matlab, etc.) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator
to:
* Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible for any topics and assignments for the missed classes.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator functions. Even during the exam, you may '''not''' use any other software (e.g., R, python, matlab, etc.) for the computations.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator functions. Even during the exam, you may '''not''' use any other software (e.g., R, python, matlab, etc.) for the computations.
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[l] DA: Graph Analysis
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[l] DA: Graph Analysis
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[l] DA: Graph Analysis & Mining
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[l] Data Mining and Analysis (DA): Introduction
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[l] Data Mining and Analysis (DA): Algebraic and Probabilistic Views
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[l]DA: Algebraic and Probabilistic Views
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[l]DA: Numeric Attributes
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[l]R: Sep 20
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[l]R: Sep 20
[l] DA: High Dimensional Analysis
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[l]R: Sep 27
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[row bgcolor=aliceblue]
[l]R: Sep 27
[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] FPM: Sequence Mining
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[l] FPM: Sequence Mining
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[l] DA: Graph Analysis
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[l] DA: Graph Analysis
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[l] FPM: Graph Mining
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[l] FPM: Graph Mining
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[l] FPM: Pattern Assessment
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[l] FPM: Graph Mining
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[l] Classification (CLASS): Bayesian Classifier
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[l] FPM: Pattern Assessment
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[l] CLASS: Decision Trees
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[l] , Classification (CLASS): Linear Discriminants
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[l] CLASS: Linear Discriminants & SVMs
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[row bgcolor=aliceblue]
[l]R: Nov 15
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[row bgcolor=aliceblue]
[l]R: Nov 15
[l] Clustering (CLUS): Hierarchical, Partitional
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[l] Clustering (CLUS): Hierarchical, Partitional
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[l] CLUS: Density-based Clustering
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[l] CLUS: Density-based Clustering
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[l] CLUS: Spectral & Graph Clustering
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[l] CLUS: Spectral & Graph Clustering
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[l] CLUS: Subspace Clustering
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[l]DA: Numeric Attributes
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[l] Data Mining and Analysis (DA): Introduction
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[l]DA: Numeric Attributes & Eigenvectors
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[l]DA: Algebraic and Probabilistic Views
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[l] DA: Categorical Data
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[l] DA: Numeric Attributes
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[l] DA: Graph Data
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[l] DA: Kernel Approach
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[l] DA: Graph Models
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[l] DA: High Dimensional Analysis
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[l] DA: Kernel Methods
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[l] DA: Dimensionality Reduction
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[l]DA: High Dimensional Analysis
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l]DA: Dimensionality Reduction
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[l] FPM: Sequence Mining
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[l] Frequent Pattern Mining (FPM): Itemset Mining
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[l] DA: Graph Analysis
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[l] FPM: Itemset Summaries & Sequence Mining
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[l] FPM: Graph Mining
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[l] FPM: Sequence Mining, Graph Mining
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[l] FPM: Graph Mining
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[l] FPM: Graph Mining, Classification (CLASS): Linear Discriminants
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[l] FPM: Pattern Assessment
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[l] CLASS: SVMs
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[l] , Classification (CLASS): Linear Discriminants
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[l] CLASS: Bayesian Classifier, Decision Trees
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[l] CLASS: SVMs
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[l] Clustering (CLUS): Partitional
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[l] CLASS: Bayesian Classifier, Decision Trees
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[l] CLUS: Hierarchical Clustering
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[l] CLASS: Classifier Evaluation
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[l] CLUS: Density-based Clustering,
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[l] Clustering (CLUS): Hierarchical, Partitional
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[l] CLUS: Subspace Clustering
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[l] CLUS: Density-based Clustering
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[l] Spectral & Graph Clustering
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[l] CLUS: Spectral & Graph Clustering
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[l] Evaluation & Assessment
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[l] CLUS: Evaluation & Assessment
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[l] CLASS: Linear Discriminants, Support Vector Machines (SVM)
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[l] %blue% NO CLASS%%
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[l] CLASS: SVMs
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[l] %red%'''EXAM II'''%%
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[l]%red%'''EXAM II'''%%
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[l] CLASS: SVMs
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* Aug 6: Students in the class must sign up for the [[https://piazza.com/class#fall2012/cs43906390 | Piazza]] course discussion site. All discussions and Q&A will be carried out using Piazza.
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[!c]Chapters
[!c]Lecture Notes
[!c]Lecture Notes
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[!c]Readings
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[l] %blue%NO CLASS%
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[l] %blue%NO CLASS%%
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[l] [[(Attach:)lecture2.pdf]]
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[l] [[(Attach:)lecture3.pdf]]
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[l] [[(Attach:)lecture4.pdf]]
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[l] [[(Attach:)lecture5.pdf]]
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[l] [[(Attach:)lecture6.pdf]]
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[l] [[(Attach:)lecture7.pdf]]
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[l] [[(Attach:)lecture8.pdf]]
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[l] [[(Attach:)lecture9.pdf]]
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[l] [[(Attach:)lecture10.pdf]]
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[l] [[(Attach:)lecture11.pdf]]
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[l] [[(Attach:)lecture12.pdf]]
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[l] [[(Attach:)lecture13.pdf]]
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[l] [[(Attach:)lecture14.pdf]]
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[l] [[(Attach:)lecture15.pdf]]
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[l] [[(Attach:)lecture16.pdf]]
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[l] [[(Attach:)lecture17.pdf]]
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[l] [[(Attach:)lecture18.pdf]]
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[l] [[(Attach:)lecture19.pdf]]
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[l] [[(Attach:)lecture20.pdf]]
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[l] [[(Attach:)lecture21.pdf]]
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[l] [[(Attach:)lecture22.pdf]]
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* Nov 70: [[Dmcourse/Assign6|Assignment 6]] has been posted.
* Nov 7: [[Dmcourse/Assign5|Assignment 5]] has been posted.
* Oct 25: updated chap8.pdf on PCA, kernel PCA and SVD.
* Oct 24: [[Dmcourse/Assign4|Assignment 4]] has been posted.
* Oct 14: [[Dmcourse/Assign3|Assignment 3]] has been posted.
* Sep 25: [[Dmcourse/Assign2|Assignment 2]] has been posted.
* Sep 17: [[Dmcourse/Assign1|Assignment 1]] has been posted.
* Sep 14: Activate your [[http://piazza.com/class#fall2011/cs43906390 | piazza account]]
* Sep 12: Book chapters, as well as lectures are posted online after each lecture. Make sure to check the course website.
* Aug 18: Course website is up, with the tentative calendar and syllabus.
* Nov 7: [[Dmcourse/Assign5|Assignment 5]] has been posted.
* Oct 25: updated chap8.pdf on PCA, kernel PCA and SVD.
* Oct 24: [[Dmcourse/Assign4|Assignment 4]] has been posted.
* Oct 14: [[Dmcourse/Assign3|Assignment 3]] has been posted.
* Sep 25: [[Dmcourse/Assign2|Assignment 2]] has been posted.
* Sep 17: [[Dmcourse/Assign1|Assignment 1]] has been posted.
* Sep 14: Activate your [[http://piazza.com/class#fall2011/cs43906390 | piazza account]]
* Sep 12: Book chapters, as well as lectures are posted online after each lecture. Make sure to check the course website.
* Aug 18: Course website is up, with the tentative calendar and syllabus
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* Aug 6: Course website is up, with the syllabus and tentative calendar.
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[l]M: Aug 29
[l]%blue%CLASSES CANCELLED%%
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[l]M: Aug 27
[l]%blue%NO CLASS%%
[l]%blue%NO CLASS%%
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[l]R: Sep 1
[l] Data Mining Overview & Data Analysis Foundations (DA): Algebraic & Probabilistic Views
[l] [[(Attach:)chap1.pdf]]
[l] [[Attach:dmintro.pptx]],[[(Attach:)lecture1.pdf]]
[l] [[(Attach:)chap1.pdf]]
[l] [[Attach:dmintro.pptx]],[[(Attach:)lecture1.pdf]]
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[l]R: Aug 30
[l] %blue%NO CLASS%
[l] %blue%NO CLASS%
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[l]M: Sep 5
[l]%blue%Labor Day Holiday%%
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[l]M: Sep 3
[l]%red%Labor Day Holiday%%
[l]%red%Labor Day Holiday%%
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[l]R: Sep 8
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[l]R: Sep 6
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[l]M: Sep 12
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[l]M: Sep 10
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[l]R: Sep 15
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[l]R: Sep 13
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[l]M: Sep 19
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[l]M: Sep 17
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[l]R: Sep 20
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!!%center% CSCI-4390/6390: Data Mining, Fall 2011
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!!%center% CSCI-4390/6390: Data Mining, Fall 2012
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'''Room''': Carnegie 113\\
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'''Room''': TBA\\
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'''TA''': Amina Shabbeer\\
'''TA Office Hours''':4-5PM, TW, AE 304\\
'''TA Contact''':shabba@rpi.edu
'''TA Office Hours''':
'''TA Contact''':
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'''TA''': TBA\\
'''TA Office Hours''': TBA\\
'''TA Contact''': TBA
'''TA Office Hours''': TBA\\
'''TA Contact''': TBA
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[l] [[(Attach:)chap21.pdf]]
[l] [[(Attach:)lecture22.pdf]]
[l] [[(Attach:)lecture22.pdf]]
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[l] CLUS: Subspace Clustering, Spectral & Graph Clustering
[l] [[(Attach:)chap19.pdf]], [chap20.pdf]]
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[l] CLUS: Subspace Clustering
[l] [[(Attach:)chap19.pdf]]
[l] [[(Attach:)lecture20.pdf]]
[l] [[(Attach:)chap19.pdf]]
[l] [[(Attach:)lecture20.pdf]]
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[l] Evaluation & Assessment
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[l] Spectral & Graph Clustering
[l] [[(Attach:)chap20.pdf]]
[l] [[(Attach:)chap20.pdf]]
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[l] CLUS: Density-based Clustering, Subspace Clustering
[l] [[(Attach:)chap18.pdf]], [chap19.pdf]]
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[l] CLUS: Density-based Clustering,
[l] [[(Attach:)chap18.pdf]]
[l] [[(Attach:)lecture19.pdf]]
[l] [[(Attach:)chap18.pdf]]
[l] [[(Attach:)lecture19.pdf]]
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[l] CLUS: Spectral & Graph Clustering
[l] [[(Attach:)chap20.pdf]]
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[l] CLUS: Subspace Clustering, Spectral & Graph Clustering
[l] [[(Attach:)chap19.pdf]], [[(Attach:)chap20.pdf]]
[l] [[(Attach:)chap19.pdf]], [[(Attach:)chap20.pdf]]
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* Nov 70: [[Dmcourse/Assign6|Assignment 6]] has been posted.
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[l] CLUS: Density-based Clustering
[l] [
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[l] CLUS: Density-based Clustering, Subspace Clustering
[l] [[(Attach:)chap18.pdf]], [[(Attach:)chap19.pdf]]
[l] [[(Attach:)chap18.pdf]], [[(Attach:)chap19.pdf]]
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[l] [[(Attach:)chap20.pdf]]
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[l]R: Dec 1
[l] Evaluation & Assessment
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[l] Evaluation & Assessment
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[l] Evaluation & Assessment
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[l] [[(Attach:)chap17.pdf]]
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[l] [[(Attach:)chap18.pdf]]
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[l] CLASS: Bayesian Classifier
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[l] CLASS: Bayesian Classifier, Decision Trees
[l] [[(Attach:)chap26.pdf]], [[(Attach:)chap24.pdf]]
[l] [[(Attach:)chap26.pdf]], [[(Attach:)chap24.pdf]]
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* Nov 7: [[Dmcourse/Assign5|Assignment 5]] has been posted.
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[l] CLASS: SVMs & Decision Trees
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[l] CLASS: SVMs
[l] [[(Attach:)lecture15.pdf]]
[l] [[(Attach:)lecture15.pdf]]
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[l] [[(Attach:)chap27.pdf]]
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[l] FPM: Graph Mining
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[l] FPM: Graph Mining, Classification (CLASS): Linear Discriminants
[l] [[(Attach:)lecture13.pdf]]
[l] [[(Attach:)chap27.pdf]]
[l] [[(Attach:)lecture13.pdf]]
[l] [[(Attach:)chap27.pdf]]
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[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)
[l] [[(Attach:)chap27.pdf]], [[(Attach:)chap28.pdf]]
[l]
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[l] CLASS: Linear Discriminants, Support Vector Machines (SVM)
[l] [[(Attach:)chap28.pdf]]
[l] [[(Attach:)chap28.pdf]]
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[l] [[(Attach:)chap27.pdf]], [[(Attach:)chap28.pdf]]
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* Oct 25: updated chap8.pdf on PCA, kernel PCA and SVD.
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* Oct 24: [[Dmcourse/Assign4|Assignment 4]] has been posted.
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[l] [[(Attach:)lecture11.pdf]]
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[l] FPM: Sequence Mining, Graph Mining
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[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)
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[l] CLASS: SVMs & Decision Trees
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[l] CLASS: Bayesian Classifier
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[l] Clustering (CLUS): Partitional
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[l] [[(Attach:)chap10.pdf]], [[(Attach:)chap11.pdf]]
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[l] [[(Attach:)chap11.pdf]], [[(Attach:)chap12.pdf]]
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[l] FPM: Sequence Mining
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[l] FPM: Itemset Summaries & Sequence Mining
[l] [[(Attach:)chap10.pdf]], [[(Attach:)chap11.pdf]]
[l] [[(Attach:)chap10.pdf]], [[(Attach:)chap11.pdf]]
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* Oct 14: [[Dmcourse/Assign3|Assignment 3]] has been posted.
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[l]DA: Dimensionality Reduction, Frequent Pattern Mining (FPM): Itemset Mining
[l] [chap8.pdf]], [[(Attach:)chap10.pdf]]
[l
to:
[l]DA: Dimensionality Reduction
[l] [[(Attach:)chap8.pdf]]
[l] [[(Attach:)lecture9.pdf]]
[l] [[(Attach:)chap8.pdf]]
[l] [[(Attach:)lecture9.pdf]]
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[l] FPM: Itemsets and Sequences
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[l] Frequent Pattern Mining (FPM): Itemset Mining
[l] [[(Attach:)chap10.pdf]]
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[l] [[(Attach:)chap10.pdf]]
[l]
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[l]%blue% NO CLASS%%
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[l] FPM: Sequence Mining
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[l] FPM: Itemsets and Sequences
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[l] Graph Mining
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[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)CLASS: SVMs
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[l] CLASS: Ensembles & Classifier Assessment
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[l] [[(Attach:)chap8.pdf]], [[(Attach:)chap9.pdf]]
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[l] [[(Attach:)chap8.pdf]], [[(Attach:)chap10.pdf]]
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[l] [[(Attach:)chap8.pdf]], [[(Attach:)chap9.pdf]]
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[l]DA: High Dimensional Analysis & Dimensionality Reduction (PCA/SVD)
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[l]DA: High Dimensional Analysis
[l] [[(Attach:)chap6.pdf]]
[l] [[(Attach:)lecture8.pdf]]
[l] [[(Attach:)chap6.pdf]]
[l] [[(Attach:)lecture8.pdf]]
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[l]Frequent Pattern Mining (FPM): Itemset Mining
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[l]DA: Dimensionality Reduction, Frequent Pattern Mining (FPM): Itemset Mining
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[l] [[(Attach:)chap5.pdf]]
[l] [[(Attach:)lecture7.pdf]]
[l] [[(Attach:)lecture7.pdf]]
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[l] DA: Graph Models, Kernel Method
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[l] DA: Graph Models
[l]
[l] [[(Attach:)lecture6.pdf]]
[l]
[l] [[(Attach:)lecture6.pdf]]
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[l] DA: High Dimensional Analysis
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[l] DA: Kernel Methods
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[l]DA: Dimensionality Reduction (PCA/SVD)
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[l]DA: High Dimensional Analysis & Dimensionality Reduction (PCA/SVD)
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* Sep 25: [[Dmcourse/Assign2|Assignment 2]] has been posted.
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[l] DA: Graph Models
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[l] DA: Graph Data
[l] [[(Attach:)chap4.pdf]]
[l] [[(Attach:)lecture5.pdf]]
[l] [[(Attach:)chap4.pdf]]
[l] [[(Attach:)lecture5.pdf]]
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[l] DA: Kernel Method
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[l] DA: Graph Models, Kernel Method
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* Sep 17: [[Dmcourse/Assign1|Assignment 1]] has been posted.
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[l] DA: Graph Data
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[l] DA: Categorical Data
[l] [[(Attach:)chap3.pdf]]
[l] [[(Attach:)lecture4.pdf]]
[l] [[(Attach:)chap3.pdf]]
[l] [[(Attach:)lecture4.pdf]]
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[l]DA: Numeric & Categorical Attributes
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[l]DA: Numeric Attributes & Eigenvectors
[l]
[l] [[(Attach:)lecture3.pdf]]
[l]
[l] [[(Attach:)lecture3.pdf]]
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* Sep 14: Activate your [[http://piazza.com/class#fall2011/cs43906390 | piazza account]]
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[l] [[Attach:chap1.pdf]]
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[l] [[(Attach:)chap1.pdf]]
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[l] [[Attach:chap2.pdf]]
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[l] [[(Attach:)chap2.pdf]]
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* Sep 12: Book chapters, as well as lectures are posted online after each lecture. Make sure to check the course website.
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[l] [[Attach:chap2.pdf]]
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'''TA Office Hours''': 4-5PM, TW, AE 217\\
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'''TA Office Hours''': 4-5PM, TW, AE 304\\
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[l]DA: Numeric Attributes
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[l] [[(Attach:)lecture2.pdf]]
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[l] [[Attach:dmintro.pptx]],[[(Attach:)lecture1.pdf]]
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[l] [[dmintro.pptx]],[[(Attach:)lecture1.pdf]]
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[l] [[(Attach:)dmintro.pptx]],[[(Attach:)lecture1.pdf]]
[l] [[(Attach:)dmintro.pptx]],[[(Attach:)lecture1.pdf]]
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'''TA''': TBA\\
'''TA Office Hours''':TBA\\
'''TA Contact''':TBA
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'''TA''': Amina Shabbeer\\
'''TA Office Hours''': 4-5PM, TW, AE 217\\
'''TA Contact''': shabba@rpi.edu
'''TA Office Hours''': 4-5PM, TW, AE 217\\
'''TA Contact''': shabba@rpi.edu
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[l]Data Mining Overview & Data Analysis Foundations (DA)
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[l]%blue%CLASSES CANCELLED%%
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[l] DA: Algebraic & Probabilistic Views
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[l] Data Mining Overview & Data Analysis Foundations (DA): Algebraic & Probabilistic Views
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You are expected to learn python on your own via web tutorials, etc.
to:
You are expected to learn python on your own via web tutorials, etc. Assignments must be submitted via email to [[hidden-email:qzpbhefr.pf@tznvy.pbz]].
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[l]EDA: Dimensionality Reduction (PCA/SVD)
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[l]DA: Dimensionality Reduction (PCA/SVD)
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[l]EDA: Frequent Pattern Mining (FPM): Itemset Mining
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[l]Frequent Pattern Mining (FPM): Itemset Mining
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[l]%blue% NO CLASS%% [[http://www.dsrc.rpi.edu/?page=news_and_events | NSF-RPI Workshop]]
to:
[l]%blue% NO CLASS%% [[http://www.dsrc.rpi.edu/?page=news_and_events | NSF-RPI Workshop on Complex Data]]
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You are expected to learn python on your own via web tutorials, etc.
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The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the [[http://www.rpi.edu/dept/doso/2008-2010RPIStudentHandbook.pdf | Rensselaer Handbook of Student Rights and Responsibilities]] on this topic.
to:
The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the [[http://www.rpi.edu/dept/doso/2010-2012RPIStudentHandbook.pdf| Rensselaer Handbook of Student Rights and Responsibilities]] on this topic.
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Your grade will be a combination of the following items. Note that the final distribution is subject to some change depending on the number of assignments, but exams will be at least 60%.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to run some mining methods on some real datasets, or to implement some algorithms, to complement the theory. There will be roughly one assignment per week, to be submitted via the course wiki site. User accounts will be created after first day of class.
* Assignments (40%): The assignments are meant to
to:
Your grade will be a combination of the following items.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to implement some algorithms and apply them to real datasets, to complement the theory. There will be roughly one assignment every two weeks.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to implement some algorithms and apply them to real datasets, to complement the theory. There will be roughly one assignment every two weeks.
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* Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible entirely responsible for brushing up on any missed material.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, etc) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, etc) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
to:
* Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible for brushing up on any missed material.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, matlab, etc.) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, matlab, etc.) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
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Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic foundations.
to:
Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: algebraic and statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic approach.
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The pre-requisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also essentially pre-requisites, though an attempt will be made to review the basic concepts. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
to:
The pre-requisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also essentially pre-requisites, though an attempt will be made to review the basic concepts. Assignments will require the use of the [[http://www.python.org | python]] language, with [[http://numpy.scipy.org/| NumPy]] package for numeric computations.
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[l] Classification (CLASS): Linear Discriminants
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[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)
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[l]R: Oct 27
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[l] CLASS: Decision Trees
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[l] CLASS: Ensembles & Classifier Assessment
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[l] CLASS: Ensembles & Classifier Assessment
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[l] Clustering (CLUS): Partitional
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[l] CLUS: Partitional
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[l]DA: Numeric & Categorical Attributes
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[l]DA: Numeric & Categorical Attributes
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[l]R: Sep 29
[l] DA: High Dimensional Analysis
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[l]EDA: Dimensionality Reduction (PCA/SVD)
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[l]EDA: Dimensionality Reduction (PCA/SVD)
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[l]EDA: Frequent Pattern Mining (FPM): Itemset Mining
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[l]Frequent Pattern Mining (FPM): Itemset Mining
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[l] FPM: Sequence Mining
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[l] FPM:Graph Mining
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[l] Classification (CLASS): Linear Discriminants
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[l] Classification (CLASS): Linear Discriminant Analysis (LDA)
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[l] CLASS: Support Vector Machines (SVM)
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[l] CLASS: SVMs
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[l] CLASS: Decision Trees
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[l]CLASS: Bayesian Classifier
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[l] CLASS: Bayesian Classifier
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[l] CLASS: Decision Trees & Classifier Assessment
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[l] CLASS: Ensembles & Classifier Assessment
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[l]Data Mining Overview
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[l]Data Mining Overview & Data Analysis Foundations (DA)
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[l]Exploratory Data Analysis (EDA): Data Matrix
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[l] DA: Algebraic & Probabilistic Views
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[l]DA: Numeric Attributes
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[l]Categorical Attributes
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[l]DA: Categorical Attributes
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[l] DA: Graph Data
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[l] EDA: Graph Data Analysis
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[l] DA: Graph Models
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[l] EDA: Web Centralities
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[l] DA: Graph Models
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[l] DA: Kernel Method
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[l]EDA: High Dimensional Analysis & Dimensionality Reduction (PCA/SVD)
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[l]EDA: Dimensionality Reduction (PCA/SVD)
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[l]Classification (CLASS): Linear Discriminant Analysis (LDA)
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[l] Classification (CLASS): Linear Discriminant Analysis (LDA)
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[l]CLASS: Decision Trees & Classifier Assessment
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[l] CLASS: Decision Trees & Classifier Assessment
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[l]Clustering (CLUS): Partitional
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[l] Clustering (CLUS): Partitional
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[l]CLUS: Hierarchical Clustering
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[l] CLUS: Hierarchical Clustering
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[l]CLUS: Density-based Clustering
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[l] CLUS: Density-based Clustering
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[l]CLUS: Subspace Clustering
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[l] CLUS: Subspace Clustering
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[l]CLUS: Spectral Clustering
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[l] CLUS: Spectral & Graph Clustering
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[l]CLUS: Kernel K-means
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[l] CLUS: Graph Clustering
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[l]M: Sep 5
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[l]M: Sep 12
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* Sep 26: [[Dmcourse/Assign2]] posted.
* Sep 26: Check the chapter notes often for updates. Usually there is a date printed on top to indicate if there is a new version.
* Sep 21: Pranay will hold TA hours on 22nd (wed) between 12-1:45pm; he will not hold hours on friday (23rd).
* Sep 15: [[Dmcourse/Assign1]] posted. You may also want to check ou the [[Dmcourse/Pmwiki]] guidelines and the [[http://www.statmethods.net | Quick R Tutorial]].
* Sep 3: Accounts for the Assignment page were mailed out. Contact me if you did not get that.
* Sep 1: First three chapters now posted online.
* Aug 2: Course website is up, with the tentative calendar and syllabus.
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* Sep 26: Check the chapter notes often for updates. Usually there is a date printed on top to indicate if there is a new version.
* Sep 21: Pranay will hold TA hours on 22nd (wed) between 12-1:45pm; he will not hold hours on friday (23rd).
* Sep 15: [[Dmcourse/Assign1]] posted. You may also want to check ou the [[Dmcourse/Pmwiki]] guidelines and the [[http://www.statmethods.net | Quick R Tutorial]].
* Sep 3: Accounts for the Assignment page were mailed out. Contact me if you did not get that.
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!!%center% CSCI-4390/6390: Data Mining, Fall 2010
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'''TA''': Pranay Anchuri\\
'''TA Office Hours''':12:00-1:50PM TF\\
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[l]R: Sep 30
[l]EDA: High Dimensional Data
[l]R: Sep 30
[l]EDA: High Dimensional Data
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[l]FPM: Itemset Summaries
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[l]EDA: Dimensionality Reduction (PCA)
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[l] CLASS: Linear Discriminant Analysis (LDA)
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[l]FPM: Pattern Significance
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CLASS: Probabilistic Methods
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[l] CLASS: Probabilistic Methods
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[l] CLASS: Linear Discriminant Analysis (LDA)
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[l]R: Oct 28
[row bgcolor=aliceblue]
[l]R: Oct 28
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[l]R: Oct 28
[l]CLASS: Kernel SVMs, Graph Classification
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[l]CLASS: Classifier Evaluation
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[l]CLASS: Kernel SVMs
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[l]CLUS: Hierarchical Clustering
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[l]CLASS: Graph Classification
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[l]CLUS: Density-based Clustering
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[l]CLASS: Classifier Evaluation
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[l]CLUS: Subspace Clustering
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[l]CLUS: Hierarchical Clustering
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[l]CLUS: Spectral Clustering
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[l]CLUS: Density-based Clustering
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[l]CLUS: Graph Clustering
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[l]CLUS: Subspace Clustering
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[l]Cluster Evaluation
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[l]CLUS: Spectral Clustering
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[l]Social Network Analysis (SNA)
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[l]CLUS: Graph Clustering
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[l]SNA: Graph Mining
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[l]Cluster Evaluation
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[l]EDA: Numeric & Categorical Attributes
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[l]EDA: Categorical Attributes
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[l]Frequent Pattern Mining (FPM): Itemset Mining
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[l]EDA: Eigenvalues Primer; Graph Data Analysis
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[l]Clustering (CLUS): Partitional (KMeans, EM)
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[l]EDA: High Dimensional Data
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[l]Classification (CLASS): Decision Trees
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[l]EDA: Dimensionality Reduction (PCA)
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[l]EDA: High Dimensional Data
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[l]Frequent Pattern Mining (FPM): Itemset Mining
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[l]EDA: Dimensionality Reduction: PCA
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[l]Clustering (CLUS): Partitional (KMeans, EM)
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[l]EDA: Dimensionality Reduction: PCA/SVD
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[l]Classification (CLASS): Decision Trees
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[l]FPM: Itemset Summaries
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[row bgcolor=aliceblue]
[l]R: Oct 14
[l]FPM:Graph Mining
[l]R: Oct 14
[l]FPM:Graph Mining
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[l]FPM:Sequence Mining, CLASS: Probabilistic
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[l] CLASS: Linear Discriminant Analysis (LDA)
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CLASS: Probabilistic Methods
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[l]CLASS: SVM contd.
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[l]CLASS: Support Vector Machines (SVM)
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[l]CLASS: Kernel SVM, Rule-based
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[l]CLASS: Kernel SVMs, Graph Classification
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[l]CLUS: Hierarchical/Density-based Clustering
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[l]CLUS: Hierarchical Clustering
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[l]CLUS: Density-based Clustering (Kernel Density Estimation)
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[l]CLUS: Density-based Clustering
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[l]Kernel Methods: Kernel K-means
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[l]CLUS: Graph Clustering
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[l]Kernel Methods: Kernel PCA/LDA
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[l]Cluster Evaluation
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* knowledgeable about the fundamental data mining tasks like pattern mining, classification and clustering
* able tounderstand the key algorithms for the main tasks
* able to
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* able to describe the fundamental data mining tasks like pattern mining, classification and clustering
* able to analyze the key algorithms for the main tasks
* able to analyze the key algorithms for the main tasks
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!!![[Announcements#]]%red%Announcements%%
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!!![[#Announcements]]%red%Announcements%%
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!!![[Calender#
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!!![[#Calendar]]Calendar & Lecture Notes
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!!!%red%Announcements%%
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!!![[Announcements#]]%red%Announcements%%
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!!!Calendar & Lecture Notes
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!!![[Calender#]]Calendar & Lecture Notes
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There is no required text for the course. Notes will be handed out in class.
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There is no required text for the course. Notes will be posted online on the course webpage.
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* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam.
'''Attendance''': Studentsstrongly encouraged to participate in the class, and should try to attend all classes.
'''Attendance''': Students
to:
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
!!!!! Other Policies
* Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible entirely responsible for brushing up on any missed material.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, etc) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
* Late Assignments: Most assignments will be due just before midnight on the due date. Students get an automatic one day extension with 20% penalty. No late assignments will be accepted after the midnight following the due date.
!!!!! Other Policies
* Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible entirely responsible for brushing up on any missed material.
* Laptops: Absolutely no laptops will be allowed in class during lectures. The only exception is during exams, to access the class notes online and to use the calculator. Even during the exam, you may '''not''' use any other software (e.g., R, python, etc) for the computations, and you may not "browse" for solutions (you are not likely to find anything!).
* Late Assignments: Most assignments will be due just before midnight on the due date. Students get an automatic one day extension with 20% penalty. No late assignments will be accepted after the midnight following the due date.
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You may consult other members of the class on the homeworks, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
to:
You may consult other members of the class on the assignments, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
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'''TA Contact''': AE106, x2857, mailto:anchup@rpi.edu
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'''TA Contact''': AE106, x2857, [[hidden-email:napuhc@ecv.rqh]]
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'''TA Contact''': AE106, x2857, anchup@rpi.edu
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'''TA Contact''': AE106, x2857, mailto:anchup@rpi.edu
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'''TA Office Hours''': TBD\\
'''TA Contact''':
'''TA Contact''':
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'''TA Office Hours''': 12:00-1:50PM TF\\
'''TA Contact''': AE106, x2857, anchup@rpi.edu
'''TA Contact''': AE106, x2857, anchup@rpi.edu
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'''Class''': 10-11:50AM, MR, Room: Carnegie 113\\
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'''Class Time''': MR 10-11:50AM\\
'''Room''': Carnegie 113\\
'''Room''': Carnegie 113\\
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'''TA & TA Office Hours''': Pranay Anchuri, Hours TBD
to:
----
'''TA''': Pranay Anchuri\\
'''TA Office Hours''': TBD\\
'''TA Contact''':
'''TA''': Pranay Anchuri\\
'''TA Office Hours''': TBD\\
'''TA Contact''':
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'''Class''': 10-11:50AM, MR, Room: TBD\\
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'''Class''': 10-11:50AM, MR, Room: Carnegie 113\\
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'''TA & TA Office Hours''': TBD
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'''TA & TA Office Hours''': Pranay Anchuri, Hours TBD
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!!!Calendar & Lecture Notes/Videos
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!!!Calendar & Lecture Notes
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[l]%red%'''EXAM III'''%%
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[l]Social Network Analysis (SNA)
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[l]R: Dec 9
[l]%red%'''EXAM III'''%%
[l]R: Dec 9
[l]%red%'''EXAM III'''%%
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The pre-requisites for this course include data structures and algorithms and discrete mathematics.
Basics of linear algebra, and probability & statistics will be very useful as well.
Basics of linear algebra, and probability & statistics will be very useful as well.
to:
The pre-requisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also essentially pre-requisites, though an attempt will be made to review the basic concepts. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
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You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
to:
You may consult other members of the class on the homeworks, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
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!!%center% CSCI-4390/6390: Data Mining, Fall 2009
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!!%center% CSCI-4390/6390: Data Mining, Fall 2010
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'''Class''': 10-11:50AM, MR, Low 3045\\
'''Instructor Office Hours''': 12-1PM,MR
'''Instructor Office Hours''': 12-1PM,
to:
'''Class''': 10-11:50AM, MR, Room: TBD\\
'''Instructor Office Hours''': 12-1PM, MR\\
'''TA & TA Office Hours''': TBD
'''Instructor Office Hours''': 12-1PM, MR\\
'''TA & TA Office Hours''': TBD
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* Dec 4: [[((Attach:)exam3-sol.pdf | Exam III solutions]] have been posted.
* Dec 2: Solutions to [[Dmcourse/Assign6 | Assignment 6]] posted on the assignment page
* Nov 17: [[Dmcourse/Assign6 | Assignment 6]] posted.
* Nov 12: [[((Attach:)exam2-sol.pdf | Exam II solutions]] have been posted.
* Nov 4: Solutions to [[Dmcourse/Assign5 | Assignment 5]] posted on the assignment page.
* Oct 31: Solutions to [[Dmcourse/Assign4 | Assignment 4]] posted on the assignment page.
* Oct 24: [[Dmcourse/Assign5 | Assignment 5]] posted.
* Oct 13: [[((Attach:)exam1-sol.pdf | Exam I solutions]] have been posted.
* Oct 10: [[Dmcourse/Assign4 | Assignment 4]] has been posted.
* Oct 4: Solutions for Assignment 3 posted.
* Sep 27: Solutions for Assignments 1 and 2 have been posted on the respective pages.
* Sep 26: [[Dmcourse/Assign3 | Assignment 3]] is now available.
* Sep 18: [[Dmcourse/Assign2 | Assignment 2]] is now available.
* Sep 12: I have posted the notes below. They are time-stamped so that if I update them, you can check if your copy is the latest one or not.
* Sep 8: Assignment 1 has been posted. See the general R/pmwiki instruction at [[Dmcourse/Assignments]] and see the specific assignment at [[Dmcourse/Assign1]]
* Sep 2: Passwords for the assignment submission wiki were sent out yesterday. Contact me if you did not get the email.
* Aug 30: Slight update of the syllabus.
* Aug 19: Course website is up, with the tentative calendar and syllabus.
* Dec 2: Solutions to [[Dmcourse/Assign6 | Assignment 6]] posted on the assignment page
* Nov 17: [[Dmcourse/Assign6 | Assignment 6]] posted.
* Nov 12: [[((Attach:)exam2-sol.pdf | Exam II solutions]] have been posted.
* Nov 4: Solutions to [[Dmcourse/Assign5 | Assignment 5]] posted on the assignment page.
* Oct 31: Solutions to [[Dmcourse/Assign4 | Assignment 4]] posted on the assignment page.
* Oct 24: [[Dmcourse/Assign5 | Assignment 5]] posted.
* Oct 13: [[((Attach:)exam1-sol.pdf | Exam I solutions]] have been posted.
* Oct 10: [[Dmcourse/Assign4 | Assignment 4]] has been posted.
* Oct 4: Solutions for Assignment 3 posted.
* Sep 27: Solutions for Assignments 1 and 2 have been posted on the respective pages.
* Sep 26: [[Dmcourse/Assign3 | Assignment 3]] is now available.
* Sep 18: [[Dmcourse/Assign2 | Assignment 2]] is now available.
* Sep 12: I have posted the notes below. They are time-stamped so that if I update them, you can check if your copy is the latest one or not.
* Sep 8: Assignment 1 has been posted. See the general R/pmwiki instruction at [[Dmcourse/Assignments]] and see the specific assignment at [[Dmcourse/Assign1]]
* Sep 2: Passwords for the assignment submission wiki were sent out yesterday. Contact me if you did not get the email.
* Aug 30: Slight update of the syllabus.
* Aug 19
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* Aug 2: Course website is up, with the tentative calendar and syllabus.
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* Dec 4: [[((Attach:)exam3-sol.pdf | Exam III solutions]] have been posted.
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* Dec 2: Solutions to [[Dmcourse/Assign6 | Assignment 6]] posted on the assignment page
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* Nov 12: [((Attach:)exam2-sol.pdf | Exam II solutions]] have been posted.
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* Nov 4: Solutions to [[Dmcourse/Assign5 | Assignment 5]] posted on the assignment page.
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* Oct 31: Solutions to [[Dmcourse/Assign4 | Assignment 4]] posted on the assignment page.
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* Oct 13: [[Dmcourse/Assign5 | Assignment 5]] posted.
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* Sep 27: Solutions for Assignments 1 and 2 have been posted on the respective pages.
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* Sep 26: [[Dmcourse/Assign3 | Assignment 3]] is now available.
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[l]Exploratory Data Analysis (EDA): Numeric Attributes
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(:cellnr:) M: Aug 31 – Data Mining Overview
(:cell:) [[Attach:Dmcourse.Main/dmintro.pdf | PDF]]
(:cell:)
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(:cell:) [[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter3.pdf | PDF]]
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(:cell:) [[Attach:Dmcourse.Main/dmintro.pdf | PDF]]
(:cell:)
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(:cell:) [[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter3.pdf | PDF]]
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(:cell:) [[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter4.pdf | PDF]]
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* Sep 12: I have posted the notes below. They are time-stamped so that if I update them, you can check if your copy is the latest one or not.
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[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter3.pdf | Notes (PDF)]])([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]])
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[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter3.pdf | Notes(PDF)]])([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]])
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[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter3.pdf | Notes (PDF)]])([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]])
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[l]Sep 10 – EDA: Numeric & Categorical Attributes ([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter4.pdf |Notes (PDF)]])([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture3/lecture3.html | Video]])
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[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes ([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter3.pdf | Notes (PDF)]]) ([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]])
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[l]Sep 10 – EDA: Numeric & Categorical Attributes ([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/notes/chapter4.pdf | Notes (PDF)]])([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture3/lecture3.html | Video]])
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[[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture3/lecture3.html | Video]])
[[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture3/lecture3.html | Video]])
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[[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture3/lecture3.html | Video]])
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[l]Sep 28 – EDA: Dimensionality Reduction (PCA/SVD)
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* Sep 8: Assignment 1 has been posted. See the general R/pmwiki instruction at [[Dmcourse/Assignments]] and see the specific assignment at [[Dmcourse/Assign1]]
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[l]Aug 31 – Data Mining Overview: [[Attach:Dmcourse.Main/dmintro.pdf | PDF]]
to:
[l]Aug 31 – Data Mining Overview: ([[Attach:Dmcourse.Main/dmintro.pdf | PDF]])
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes ([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]])
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes ([[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]])
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!!!Calendar & Lecture Notes
to:
!!!Calendar & Lecture Notes/Videos
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[l]Aug 31 – Data Mining Overview]]
[l]Sep 3 – Exploratory Data Analysis (EDA): Numericand Categorical
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric
to:
[l]Aug 31 – Data Mining Overview: [[Attach:Dmcourse.Main/dmintro.pdf | PDF]]
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes [[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]]
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes [[http://www.cs.rpi.edu/~zaki/Courses/dmcourse/videos/lecture2/lecture2.html | Video]]
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[table align=center border=1]
to:
[table align=center border=1 width=100%]
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'''Class''': 10-11:50AM, MR, Low 3045
to:
'''Class''': 10-11:50AM, MR, Low 3045\\
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!!!Calendar
to:
!!!Calendar & Lecture Notes
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[l]Aug 31 – Data Mining Overview
to:
[l]Aug 31 – [[Attach:Dmcourse.Main/dmintro.pdf | Data Mining Overview]]
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* Sep 2: Passwords for the assignment submission wiki were sent out yesterday. Contact me if you did not get the email.
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!!%center% CSCI-4390/6390: Data Mining, Fall 2009
\\
>>cframe text-align=left width=40pct<<
'''Class''': 10-11:50AM, MR, Low 3045
'''Instructor Office Hours''': 12-1PM, MR
>><<
\\
----
!!!%red%Announcements%%
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 200px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
* Aug 30: Slight update of the syllabus.
* Aug 19: Course website is up, with the tentative calendar and syllabus.
(:divend:)
(:tableend:)
\\
----
!!!Calendar
A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.
[table align=center border=1]
------
[row bgcolor=lavender]
[!c] Mondays
[!c] Thursdays
------
[row]
[l]Aug 31 – Data Mining Overview
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric and Categorical
------
[row]
[l]Sep 7 – %blue%Labor Day Holiday%%
[l]Sep 10 – Frequent Pattern Mining (FPM): Itemset Mining
------
[row]
[l]Sep 14 – Clustering (CLUS): Partitional
[l]Sep 17 – Classification (CLASS): Decision Trees
------
[row]
[l]Sep 21 – EDA: High Dimensional Data
[l]Sep 24 – EDA: Dimensionality Reduction (PCA/SVD)
------
[row]
[l]Sep 28 – EDA: SVD contd.
[l]Oct 1 – EDA: Linear Discriminant Analysis (LDA)
------
[row]
[l]Oct 5– %red%'''EXAM I'''%%
[l]Oct 8 – FPM: Itemset Summaries
------
[row]
[l]Oct 13 – %green%(Monday Schedule)%% FPM: Sequence Mining
[l]Oct 15 – CLASS: Instance-based/Rule-based
------
[row]
[l]Oct 19 – CLASS: Probabilistic
[l]Oct 22 – CLASS: Support Vector Machines (SVM)
------
[row]
[l]Oct 26 – CLASS: SVM contd.
[l]Oct 29 – CLAS: Ensemble Methods
------
[row]
[l]Nov 2 – %red%'''EXAM II'''%%
[l]Nov 5 – CLUS: Hierarchical
------
[row]
[l]Nov 9 – CLUS: Density-based
[l]Nov 12 – CLUS: Subspace
------
[row]
[l]Nov 16 – CLUS: Subspace contd.
[l]Nov 19 – CLASS: Kernel Methods (Kernel SVM)
------
[row]
[l]Nov 23 – CLASS: Kernel PCA/LDA
[l]Nov 26 – %blue%Thanksgiving Break%%
------
[row]
[l]Nov 30 - CLUS: Spectral Clustering
[l]Dec 3 – %red%'''EXAM III'''%%
------
[row]
[l]Dec 7 – Social Network Analysis (SNA)
[l]Dec 10 - SNA: Graph Mining
------
[tableend]
\\
----
!!!Syllabus
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 400px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
!!!!!Introduction
Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic foundations.
!!!!!Learning Objectives
After taking this course students will be
* knowledgeable about the fundamental data mining tasks like pattern mining, classification and clustering
* able to understand the key algorithms for the main tasks
* able to implement and apply the techniques to real world datasets
!!!!!Prerequisites
The pre-requisites for this course include data structures and algorithms and discrete mathematics.
Basics of linear algebra, and probability & statistics will be very useful as well. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
!!!!!Textbook
There is no required text for the course. Notes will be handed out in class.
The following text books are also good references:
* Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
* Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.
!!!!!Grading Policy
Your grade will be a combination of the following items. Note that the final distribution is subject to some change depending on the number of assignments, but exams will be at least 60%.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to run some mining methods on some real datasets, or to implement some algorithms, to complement the theory. There will be roughly one assignment per week, to be submitted via the course wiki site. User accounts will be created after first day of class.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam.
'''Attendance''': Students are strongly encouraged to participate in the class, and should try to attend all classes.
!!!!!Academic Integrity
You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the [[http://www.rpi.edu/dept/doso/2008-2010RPIStudentHandbook.pdf | Rensselaer Handbook of Student Rights and Responsibilities]] on this topic.
(:divend:)
(:tableend:)
\\
>>cframe text-align=left width=40pct<<
'''Class''': 10-11:50AM, MR, Low 3045
'''Instructor Office Hours''': 12-1PM, MR
>><<
\\
----
!!!%red%Announcements%%
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 200px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
* Aug 30: Slight update of the syllabus.
* Aug 19: Course website is up, with the tentative calendar and syllabus.
(:divend:)
(:tableend:)
\\
----
!!!Calendar
A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.
[table align=center border=1]
------
[row bgcolor=lavender]
[!c] Mondays
[!c] Thursdays
------
[row]
[l]Aug 31 – Data Mining Overview
[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric and Categorical
------
[row]
[l]Sep 7 – %blue%Labor Day Holiday%%
[l]Sep 10 – Frequent Pattern Mining (FPM): Itemset Mining
------
[row]
[l]Sep 14 – Clustering (CLUS): Partitional
[l]Sep 17 – Classification (CLASS): Decision Trees
------
[row]
[l]Sep 21 – EDA: High Dimensional Data
[l]Sep 24 – EDA: Dimensionality Reduction (PCA/SVD)
------
[row]
[l]Sep 28 – EDA: SVD contd.
[l]Oct 1 – EDA: Linear Discriminant Analysis (LDA)
------
[row]
[l]Oct 5– %red%'''EXAM I'''%%
[l]Oct 8 – FPM: Itemset Summaries
------
[row]
[l]Oct 13 – %green%(Monday Schedule)%% FPM: Sequence Mining
[l]Oct 15 – CLASS: Instance-based/Rule-based
------
[row]
[l]Oct 19 – CLASS: Probabilistic
[l]Oct 22 – CLASS: Support Vector Machines (SVM)
------
[row]
[l]Oct 26 – CLASS: SVM contd.
[l]Oct 29 – CLAS: Ensemble Methods
------
[row]
[l]Nov 2 – %red%'''EXAM II'''%%
[l]Nov 5 – CLUS: Hierarchical
------
[row]
[l]Nov 9 – CLUS: Density-based
[l]Nov 12 – CLUS: Subspace
------
[row]
[l]Nov 16 – CLUS: Subspace contd.
[l]Nov 19 – CLASS: Kernel Methods (Kernel SVM)
------
[row]
[l]Nov 23 – CLASS: Kernel PCA/LDA
[l]Nov 26 – %blue%Thanksgiving Break%%
------
[row]
[l]Nov 30 - CLUS: Spectral Clustering
[l]Dec 3 – %red%'''EXAM III'''%%
------
[row]
[l]Dec 7 – Social Network Analysis (SNA)
[l]Dec 10 - SNA: Graph Mining
------
[tableend]
\\
----
!!!Syllabus
(:table border=1 bgcolor=aliceblue width=100%:)
(:cell:)
(:div style="height: 400px; overflow: auto; text-align: justify; padding-top: 10px; padding-left:10px; padding-right:10px;" :)
!!!!!Introduction
Data mining is the process of automatic discovery of patterns, models, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: statistical foundations, pattern mining, classification, and clustering. Emphasis will be laid on the algorithmic foundations.
!!!!!Learning Objectives
After taking this course students will be
* knowledgeable about the fundamental data mining tasks like pattern mining, classification and clustering
* able to understand the key algorithms for the main tasks
* able to implement and apply the techniques to real world datasets
!!!!!Prerequisites
The pre-requisites for this course include data structures and algorithms and discrete mathematics.
Basics of linear algebra, and probability & statistics will be very useful as well. Assignments will require the use of the [[http://www.r-project.org | R software]]. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of [[http://www.pmwiki.org/| pmwiki]] markup usage will be your responsibility.
!!!!!Textbook
There is no required text for the course. Notes will be handed out in class.
The following text books are also good references:
* Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
* Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.
!!!!!Grading Policy
Your grade will be a combination of the following items. Note that the final distribution is subject to some change depending on the number of assignments, but exams will be at least 60%.
* Assignments (40%): The assignments are meant to be practically oriented. You'll be asked to run some mining methods on some real datasets, or to implement some algorithms, to complement the theory. There will be roughly one assignment per week, to be submitted via the course wiki site. User accounts will be created after first day of class.
* Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam.
'''Attendance''': Students are strongly encouraged to participate in the class, and should try to attend all classes.
!!!!!Academic Integrity
You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the [[http://www.rpi.edu/dept/doso/2008-2010RPIStudentHandbook.pdf | Rensselaer Handbook of Student Rights and Responsibilities]] on this topic.
(:divend:)
(:tableend:)