Main

[l] Attach:Lecture22.PDF

[l] Attach:chap11.pdf

[l] Attach:chap8.pdf, Attach:chap9.pdf

[l] FPM: Sequence Mining [l] Attach:chap10.pdf [l] Attach:Lecture21.PDF

[l] Attach:Lecture20.PDF

[l] Attach:chap8.pdf, Attach:chap10.pdf

[l] Attach:Lecture19.PDF

[l] CLUS: Spectral & Graph Clustering [l]

[l] [l] Attach:Lecture18.PDF

[l] CLUS: Evaluation & Assessment [l] Attach:chap18.pdf

[l] Frequent Pattern Mining (FPM): Itemset Mining

[l] FPM: Sequence Mining

[l] FPM: Graph Mining

[l] Attach:chap18.pdf

[l] Attach:Lecture17.PDF

[l] Attach:chap19.pdf

[l] Attach:chap17.pdf [l]

[l] Attach:Lecture16.PDF

• Nov 8: Assign5 has be posted. It is due on 16th Nov, before midnight.

[l] Attach:chap14.pdf, Attach:chap15.pdf

[l] CLUS: EM-based [l] [l] Attach:Lecture15.PDF

[row bgcolor=aliceblue] [l]R: Nov 8

[l] Attach:chap15.pdf, Attach:chap15.pdf

[l] Attach:Lecture14.PDF

[l] Clustering (CLUS): Partitional [l] Attach:chap13.pdf

[l] CLUS: Hierarchical, Density-based Clustering

• Oct 23: Assign4 has be posted. It is due on 30th Oct, before midnight.

[l]

[l] CLASS: Classifier Evaluation [l] Attach:Lecture13.PDF

[l] Attach:Lecture12.PDF

[l] Attach:chap24.pdf

[l] Attach:Lecture11.PDF

[l] Attach:chap21.pdf, Attach:chap19.pdf

[l] [l] Attach:chap21.pdf, Attach:chap19.pdf

• Oct 12: Assign3 has be posted. It is due on 19th Oct, before midnight.

[l] Attach:chap22.pdf

[l] Attach:chap23.pdf [l] Attach:Lecture10.PDF

[l] Attach:Lecture9.PDF

[l] CLASS: SVMs

[l] CLASS: Bayesian Classifier, Decision Trees

[l] Attach:chap22.pdf, Attach:chap23.pdf

[l] DA: Kernels [l] [l] Attach:Lecture8.PDF

[l] Classification (CLASS): Linear Discriminants, SVMs [l] Attach:chap22.pdf [l]

[l] Attach:chap22.pdf [l]

[l] DA: Kernels, Classification (CLASS): Linear Discriminants

[l] CLASS: SVMs, Bayesian Classifier

[l] Classification (CLASS): Linear Discriminants & SVMs

[l] CLASS: SVMs

[l] CLASS: Bayesian Classifier CL

[l] ASS: Decision Trees

[l] Attach:Lecture7.PDF

• Sep 24: Assign2 has be posted. It is due on 1st Oct, before midnight.

[l] DA: Categorical Data & [l] Attach:chap3.pdf [l] Attach:Lecture6.PDF

[l] DA: Kernel Methods [l] Attach:chap5.pdf

[l] DA: High Dimensional Analysis [l] Attach:chap6.pdf [l] Attach:Lecture5.PDF

[l] DA: Categorical Data & Kernel Methods [l] Attach:chap3.pdf, Attach:chap5.pdf

[l] Attach:Lecture4.PDF

[l] DA: Dimensionality Reduction [l] Attach:chap7.pdf

[l] DA: Categorical Data & High Dimensional Analysis [l] Attach:chap3.pdf, Attach:chap6.pdf

[l] Attach:chap3.pdf , Attach:chap7.pdf

• Sep 14: Assign1 has be posted. It is due on 21st Sep, before midnight.

• Sep 14: [Dmcourse/Assign1 | Assign1]] has be posted. It is due on 21st Sep, before midnight.

[l] DA: Numeric Attributes

[l] DA: Numeric Attributes: Eigen-decomposition [l]

[l] DA: Dimensionality Reduction & Categorical Data [l] Attach:chap3.pdf

[l] Kernel Methods

[l] Attach:Lecture3.PDF

[l] Attach:chap1.pdf

[l] Attach:chap2.pdf [l] Attach:Lecture2.PDF

[l] DA: Numeric and Categorical Attributes [l] Attach:chap3.pdf

[l] DA: Kernel Approach and Graph Analysis

• 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.

[!c]Lectures

[l] Attach:dmintro.pptx, Attach:Lecture1.PDF

[l] Attach:chap1.pdf, Attach:chap2.pdf

[l] Attach:chap3.pdf

[l] (Attach:)chap1.pdf, (Attach:)chap2.pdf

[l] (Attach:)chap3.pdf

[l] CLASS: SVMs

[l] CLUS: Evaluation & Assessment

[l] Frequent Pattern Mining (FPM): Itemset Mining

[l] FPM: Sequence Mining

[l] FPM: Graph Mining

[l] FPM: Pattern Assessment

[l] DA: Numeric and Categorical Attributes

[l] DA: Kernel Approach

[l] DA: Graph Analysis

[l] DA: High Dimensional Analysis

[l] DA: Dimensionality Reduction

[l] DA: Dimensionality Reduction

[l] Classification (CLASS): Bayesian Classifier

[l] CLASS: Decision Trees

[l] CLASS: Linear Discriminants & SVMs

[l] CLASS: SVMs FPM: Pattern Assessment

[l] CLASS: Classifier Evaluation

[l] CLASS: Classifier Evaluation

[l] Clustering (CLUS): Hierarchical, Partitional

[l] CLUS: Density-based Clustering

[l] CLUS: Subspace Clustering

[l] CLUS: Spectral & Graph Clustering

[l] CLUS: Spectral & Graph Clustering

[l] CLUS: Evaluation & Assessment

[l] Frequent Pattern Mining (FPM): Itemset Mining

[l] FPM: Sequence Mining

[l] FPM: Graph Mining

TA Office Hours: W 2-4PM, Amos Eaton 119\\

TA Contact:

TA Contact:

TA Contact: email:talkun@rpi.edu

TA Contact: emailto:talkun@rpi.edu

TA Contact: email:talkun@rpi.edu

Room: Greene 120\\

TA: Nilothpal Talukder\\

TA Contact: ehidden-email:gnyhxa@ecv.rqh

[l] DA: Numeric Attributes

[l] DA: Categorical Attributes

[l]Thanksgiving Break

Instructor Office Hours: MR 12-1PM, Lally 307\\

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.

• 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.

[l] DA: Graph Analysis

[l] DA: Graph Analysis & Mining

[l] Data Mining and Analysis (DA): Algebraic and Probabilistic Views

[l]DA: Numeric Attributes

[row bgcolor=aliceblue] [l]R: Sep 20 [l] DA: High Dimensional Analysis

[row bgcolor=aliceblue] [l]R: Sep 27 [l] Frequent Pattern Mining (FPM): Itemset Mining

[l] FPM: Sequence Mining

[l] DA: Graph Analysis

[l] FPM: Graph Mining

[l] FPM: Pattern Assessment

[l] Classification (CLASS): Bayesian Classifier

[l] CLASS: Decision Trees

[l] CLASS: Linear Discriminants & SVMs

[row bgcolor=aliceblue] [l]R: Nov 15 [l] Clustering (CLUS): Hierarchical, Partitional

[l] CLUS: Density-based Clustering

[l] CLUS: Spectral & Graph Clustering

[l] CLUS: Subspace Clustering

[l] Data Mining and Analysis (DA): Introduction

[l]DA: Algebraic and Probabilistic Views

[l] DA: Numeric Attributes

[l] DA: Kernel Approach

[l] DA: High Dimensional Analysis

[l] DA: Dimensionality Reduction

[l] Frequent Pattern Mining (FPM): Itemset Mining

[l] FPM: Sequence Mining

[l] DA: Graph Analysis

[l] FPM: Graph Mining

[l] FPM: Graph Mining

[l] FPM: Pattern Assessment

[l] , Classification (CLASS): Linear Discriminants

[l] CLASS: SVMs

[l] CLASS: Bayesian Classifier, Decision Trees

[l] CLASS: Classifier Evaluation

[l] Clustering (CLUS): Hierarchical, Partitional

[l] CLUS: Density-based Clustering

[l] CLUS: Spectral & Graph Clustering

[l] CLUS: Evaluation & Assessment

[l] NO CLASS

[l] EXAM II

[l] CLASS: SVMs

• Aug 6: Students in the class must sign up for the Piazza course discussion site. All discussions and Q&A will be carried out using Piazza.

[!c]Readings

[l] NO CLASS

• Aug 6: Course website is up, with the syllabus and tentative calendar.

[l]M: Aug 27 [l]NO CLASS

[l]R: Aug 30 [l] NO CLASS%

[l]M: Sep 3 [l]Labor Day Holiday

[l]R: Sep 6

[l]M: Sep 10

[l]R: Sep 13

[l]M: Sep 17

[l]R: Sep 20

[l]M: Sep 24

[l]R: Sep 27

[l]M: Oct 1

[l]R: Oct 4

[l]Tue: Oct 9

[l]R: Oct 11

[l]M: Oct 15

[l]R: Oct 18

[l]M: Oct 22

[l]R: Oct 25

[l]M: Oct 29

[l]R: Nov 1

[l]M: Nov 5

[l]R: Nov 8

[l]M: Nov 12

[l]R: Nov 15

[l]M: Nov 19

[l]R: Nov 22

[l]M: Nov 26

[l]R: Nov 29

[l]M: Dec 3

[l]R: Dec 6

CSCI-4390/6390: Data Mining, Fall 2012

Room: TBA\\

TA: TBA
TA Office Hours: TBA
TA Contact: TBA

[l] chap21.pdf [l] lecture22.pdf

[l] lecture21.pdf

[l] CLUS: Subspace Clustering [l] chap19.pdf [l] lecture20.pdf

[l] Spectral & Graph Clustering [l] chap20.pdf

[l] CLUS: Density-based Clustering, [l] chap18.pdf [l] lecture19.pdf

[l] CLUS: Subspace Clustering, Spectral & Graph Clustering [l] chap19.pdf, chap20.pdf

• Nov 70: Assignment 6 has been posted.

[l] CLUS: Density-based Clustering, Subspace Clustering [l] chap18.pdf, chap19.pdf

[l] chap20.pdf [row bgcolor=aliceblue] [l]R: Dec 1 [l] Evaluation & Assessment

[l] Evaluation & Assessment

[l] lecture18.pdf

[l] chap17.pdf

[l] chap18.pdf

[l] lecture17.pdf

[l] chap16.pdf

[l] lecture16.pdf

[l] CLASS: Bayesian Classifier, Decision Trees [l] chap26.pdf, chap24.pdf

• Nov 7: Assignment 5 has been posted.

[l]

[l] CLASS: SVMs [l] lecture15.pdf

[l] lecture14.pdf

[l] chap27.pdf

[l] FPM: Graph Mining, Classification (CLASS): Linear Discriminants [l] lecture13.pdf [l] chap27.pdf

[l] CLASS: Linear Discriminants, Support Vector Machines (SVM) [l] chap28.pdf

[l] chap27.pdf, chap28.pdf

• Oct 25: updated chap8.pdf on PCA, kernel PCA and SVD.

• Oct 24: Assignment 4 has been posted.

[l] lecture12.pdf

[l] chap13.pdf

[l] lecture11.pdf

[l] FPM: Sequence Mining, Graph Mining

[row bgcolor=aliceblue] [l]R: Oct 27

[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)

[l] CLASS: SVMs & Decision Trees

[l] CLASS: Bayesian Classifier

[l] Clustering (CLUS): Partitional

[l] chap11.pdf, chap12.pdf

[l] FPM: Itemset Summaries & Sequence Mining [l] chap10.pdf, chap11.pdf

[l] lecture10.pdf

• Oct 14: Assignment 3 has been posted.

[l]DA: Dimensionality Reduction [l] chap8.pdf [l] lecture9.pdf

[l] Frequent Pattern Mining (FPM): Itemset Mining [l] chap10.pdf [l]

[l] FPM: Sequence Mining

[l] FPM: Graph Mining

[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)

[l] NO CLASS

[row bgcolor=aliceblue] [l]R: Oct 13

[l] FPM: Itemsets and Sequences

[l] Graph Mining

[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)CLASS: SVMs

[row bgcolor=aliceblue] [l]R: Oct 27

[l] CLASS: Decision Trees

[row bgcolor=aliceblue] [l]R: Nov 3

[l] chap8.pdf, chap10.pdf

[l] chap8.pdf, chap9.pdf

[l]DA: High Dimensional Analysis [l] chap6.pdf [l] lecture8.pdf

[l]DA: Dimensionality Reduction, Frequent Pattern Mining (FPM): Itemset Mining

[l] chap5.pdf [l] lecture7.pdf

[l] DA: Graph Models [l] [l] lecture6.pdf

[l] DA: Kernel Methods

[l]DA: High Dimensional Analysis & Dimensionality Reduction (PCA/SVD)

• Sep 25: Assignment 2 has been posted.

[l] DA: Graph Data [l] chap4.pdf [l] lecture5.pdf

[l] DA: Graph Models, Kernel Method

• Sep 17: Assignment 1 has been posted.

[l] DA: Categorical Data [l] chap3.pdf [l] lecture4.pdf

[l]DA: Numeric Attributes & Eigenvectors [l] [l] lecture3.pdf

• Sep 14: Activate your piazza account

[l] chap1.pdf

[l] chap2.pdf

• Sep 12: Book chapters, as well as lectures are posted online after each lecture. Make sure to check the course website.

[l] Attach:chap1.pdf

[l] Attach:chap2.pdf

TA Office Hours: 4-5PM, TW, AE 304\\

[l]DA: Numeric Attributes [l] [l] lecture2.pdf

[l] Attach:dmintro.pptx,lecture1.pdf

[l] dmintro.pptx,lecture1.pdf

[l] [l] dmintro.pptx,lecture1.pdf

TA: Amina Shabbeer
TA Office Hours: 4-5PM, TW, AE 217
TA Contact: shabba@rpi.edu

[l]CLASSES CANCELLED

[l] Data Mining Overview & Data Analysis Foundations (DA): Algebraic & Probabilistic Views

You are expected to learn python on your own via web tutorials, etc. Assignments must be submitted via email to .

[l]DA: Dimensionality Reduction (PCA/SVD)

[l]Frequent Pattern Mining (FPM): Itemset Mining

[l] NO CLASS NSF-RPI Workshop on Complex Data

You are expected to learn python on your own via web tutorials, etc.

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 Rensselaer Handbook of Student Rights and Responsibilities on this topic.

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.

• 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!).

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.

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 python language, with NumPy package for numeric computations.

[l] Classification (CLASS): Linear Discriminants, Support Vector Machines (SVM)

[row bgcolor=aliceblue] [l]R: Oct 27 [l] CLASS: Decision Trees

[row bgcolor=aliceblue] [l]R: Nov 3 [l] CLASS: Ensembles & Classifier Assessment

[l] Clustering (CLUS): Partitional

[l] CLUS: Partitional

[l]DA: Numeric & Categorical Attributes

[l]DA: Numeric & Categorical Attributes

[row bgcolor=aliceblue] [l]R: Sep 29 [l] DA: High Dimensional Analysis

[l]EDA: Dimensionality Reduction (PCA/SVD)

[l]EDA: Frequent Pattern Mining (FPM): Itemset Mining

[l] FPM: Sequence Mining

[l] FPM:Graph Mining

[l] Classification (CLASS): Linear Discriminants

[l] CLASS: Support Vector Machines (SVM)

[l] CLASS: SVMs

[l] CLASS: Decision Trees

[l] CLASS: Bayesian Classifier

[l] CLASS: Ensembles & Classifier Assessment

[l]Data Mining Overview & Data Analysis Foundations (DA)

[l] DA: Algebraic & Probabilistic Views

[l]DA: Numeric Attributes

[l]DA: Categorical Attributes

[l] DA: Graph Data

[l] DA: Graph Models

[l] DA: Graph Models

[l] DA: Kernel Method

[l]EDA: Dimensionality Reduction (PCA/SVD)

[l] Classification (CLASS): Linear Discriminant Analysis (LDA)

[l] CLASS: Decision Trees & Classifier Assessment

[l] Clustering (CLUS): Partitional

[l] CLUS: Hierarchical Clustering

[l] CLUS: Density-based Clustering

[l] CLUS: Subspace Clustering

[l] CLUS: Spectral & Graph Clustering

[l] CLUS: Graph Clustering

[l] NO CLASS NSF-RPI Workshop

[l] NO CLASS NSF-RPI Workshop%

[l]M: Sep 5

[l]R: Sep 8

[l]M: Sep 12

[l]R: Sep 15

[l]M: Sep 19

[l]R: Sep 22

[l]M: Sep 26

[l]R: Sep 29

[l]M: Oct 3

[l]R: Oct 6

[l]Tue: Oct 11

[l]R: Oct 13

[l]M: Oct 17

[l]R: Oct 20

[l]M: Oct 24

[l]R: Oct 27

[l]M: Oct 31

[l]R: Nov 3

[l]M: Nov 7

[l]R: Nov 10

[l]M: Nov 14

[l]R: Nov 17

[l]M: Nov 21

[l]R: Nov 24

[l]M: Nov 28

[l]R: Dec 1

[l]M: Dec 5

[l]R: Dec 8

[l]M: Aug 29

[l]R: Sep 1

• Aug 18: Course website is up, with the tentative calendar and syllabus.

CSCI-4390/6390: Data Mining, Fall 2011

TA: TBA
TA Office Hours: TBA
TA Contact: TBA

[l]CLUS: Kernel K-means [l] chap6.pdf, and chap17, sec 17.3 [l] Lecture23.pdf

[l] chap26.pdf

[l] chap22.pdf [l] Lecture22.pdf

[l] Lecture21.pdf

[l] Lecture20.pdf

[l] chap18.pdf [l]

[l] chap20.pdf [l]

[l] chap21.pdf [l]

• Nov 14: Assign5 posted.

[l] chap17.pdf [l]

[l] chap28.pdf

[l]CLASS: Decision Trees & Classifier Assessment [l] [l] Lecture19.pdf

[l]CLASS: Bayesian Classifier [l] [l] Lecture18.pdf

[l] CLASS: SVMs [l] [l] Lecture17.pdf

[l]CLASS: Decision Trees & Naive Bayes

• Oct 29: Assign4 posted.

[l] chap30.pdf

[l] CLASS: Support Vector Machines l] chap30.pdf [l]Lecture16.pdf

[l] chap29.pdf

[l]Classification (CLASS): Linear Discriminant Analysis (LDA) l] chap29.pdf [l]Lecture15.pdf

[l] CLASS: SVM

[l] CLASS: SVM + Decision Trees

[l]CLASS: Probabilistic Method

[l]CLASS: Clustering (CLUS): Partitional (KMeans, EM)

[l]Clustering (CLUS): Partitional

[l]Lecture14.pdf

[l] chap13.pdf

[l] chap14.pdf

[l]Lecture12.pdf

[l] chap12.pdf [l]Lecture13.pdf

• Oct 17: Assign3 posted.

[l] chap7.pdf

[l]chap9.pdf [l]Lecture11.pdf

[l] chap11.pdf

• Oct 5: updated chap5.pdf is online

[l] Chap5.pdf

[l]

[l]Lecture9.pdf

[l]EDA: High Dimensional Analysis [l] [l]Lecture10.pdf

[l]EDA: High Dimensional Analysis & Dimensionality Reduction (PCA/SVD)

[row bgcolor=aliceblue] [l]R: Oct 14

[l]FPM: Sequence Mining

[row bgcolor=aliceblue] [l]R: Oct 21

[l]Classification (CLASS): Decision Trees

[row bgcolor=aliceblue] [l]R: Oct 28

[l] CLASS: Linear Discriminant Analysis (LDA)

[row bgcolor=aliceblue] [l]R: Nov 4

[l]CLASS: Kernel SVMs

[l]Clustering (CLUS): Partitional (KMeans, EM)

[row bgcolor=aliceblue] [l]R: Nov 18

[l]CLUS: Density-based Clustering

[l]CLUS: Subspace Clustering

[row bgcolor=aliceblue] [l]R: Dec 2

[l]CLUS: Graph Clustering

[l]EDA: Graph Models

[l]EDA: High Dimensional Analysis & Dimensionality Reduction (PCA/SVD)

[l] chap5.pdf

[l] [l]Lecture8.pdf

[l] EDA: Web Centralities [l] Lecture7.pdf

[l]EDA: Graph Models & High Dimensional Data

• 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.

[l] chap4-5.pdf

• Sep 26: Assign2 posted.

Instructor Office Hours: 12-1PM, MR, Lally 307

[l] [l] Lecture7.pdf

[l] EDA: Graph Models

[l] EDA: Graph Data Analysis

[l]: EDA: Graph Models

[row bgcolor=aliceblue] [l]R: Sep 30

[l]EDA: Dimensionality Reduction (PCA/SVD)

[row bgcolor=aliceblue] [l]R: Oct 7

[l]Frequent Pattern Mining (FPM): Itemset Mining

[row bgcolor=aliceblue] [l]R: Oct 14

[l]FPM:Graph Mining

[row bgcolor=aliceblue] [l]R: Oct 21

[l] CLASS: Probabilistic Methods

[row bgcolor=aliceblue] [l]R: Oct 28

[l]CLASS: Support Vector Machines (SVM)

[row bgcolor=aliceblue] [l]R: Nov 4

[l]EXAM II

• Sep 21: Pranay will hold TA hours on 22nd (wed) between 12-1:45pm; he will not hold hours on friday (23rd).

[l]Categorical Attributes [l] [l] Lecture6.pdf

[row bgcolor=aliceblue] [l]R: Sep 23

[l]EDA: High Dimensional Data

[l]EDA: Dimensionality Reduction (PCA/SVD)

[l] Lecture5.pdf

• Sep 15: Assign1 posted. You may also want to check ou the Pmwiki guidelines and the Quick R Tutorial.

• Sep 15: Assign1 posted. You may also want to check ou the Pmwiki guidelines and the .

[l] chap3.pdf

[l]EDA: Numeric Attributes [l] [l] Lecture4.pdf

[l]Categorical Attributes

 chap3.pdf

[l] EDA: Graph Data Analysis Graph Models

[l] Lecture3.pdf

[l] intro.pptx, Lecture1.pdf

[l]Lecture2.pdf

[l] intro.pptx, Lec1.pdf

[l] intro.pptx, Lec11.pdf

[l]Lec2.pdf

[l] chap2.pdf

[l] chap3.pdf

[l] chap1.pdf

[l]Lec2.pdf

[l]Exploratory Data Analysis (EDA): Data Matrix [l] Lec2.pdf

[l]EDA: Numeric Attributes [l] chap2.pdf

[l]EDA: Categorical Attributes [l] chap3.pdf

[l]EDA: Graph Data Analysis Graph Models

[l] EDA: Graph Models

[l]EDA: High Dimensional Data

[l]EDA: Dimensionality Reduction (PCA)

[l]EDA: Dimensionality Reduction (SVD)

[l]Frequent Pattern Mining (FPM): Itemset Mining

[l]Classification (CLASS): Decision Trees

[l]Clustering (CLUS): Partitional (KMeans, EM)

• 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.

[l] chap1.pdf [l] Intro.pptx, Lec1.pdf

[l] chap2.pdf

[l] chap3.pdf

[l] [l] ,

Lecture notes and videos from last year (Fall09) are available here.

[l]EDA: Graph Data Analysis

[l]EDA: Graph Models

[row bgcolor=aliceblue] [l]R: Sep 23 [l]Clustering (CLUS): Partitional (KMeans, EM)

[row bgcolor=aliceblue] [l]R: Sep 30 [l]EDA: High Dimensional Data

[l]EDA: Dimensionality Reduction (PCA)

[l]FPM: Pattern Significance

[l] CLASS: Probabilistic Methods

[l] CLASS: Linear Discriminant Analysis (LDA)

[row bgcolor=aliceblue] [l]R: Oct 28

[l]CLASS: Kernel SVMs

[l]CLASS: Graph Classification

[l]CLASS: Classifier Evaluation

[l]CLUS: Hierarchical Clustering

[l]CLUS: Density-based Clustering

[l]CLUS: Subspace Clustering

[l]CLUS: Spectral Clustering

[l]CLUS: Graph Clustering

[l]Cluster Evaluation

[l]EDA: Categorical Attributes

[l]EDA: Eigenvalues Primer; Graph Data Analysis

[l]EDA: High Dimensional Data

[l]EDA: Dimensionality Reduction (PCA)

[l]Frequent Pattern Mining (FPM): Itemset Mining

[l]Clustering (CLUS): Partitional (KMeans, EM)

[l]Classification (CLASS): Decision Trees

[l]FPM: Itemset Summaries

[row bgcolor=aliceblue] [l]R: Oct 14 [l]FPM:Graph Mining

[l] CLASS: Linear Discriminant Analysis (LDA)

CLASS: Probabilistic Methods

[l]CLASS: Support Vector Machines (SVM)

[l]CLASS: Kernel SVMs, Graph Classification

[l]CLUS: Hierarchical Clustering

[l]CLUS: Density-based Clustering

[l]CLUS: Graph Clustering

[l]Cluster Evaluation

• able to describe the fundamental data mining tasks like pattern mining, classification and clustering
• able to analyze the key algorithms for the main tasks

Announcements

Calendar & Lecture Notes

Announcements#Announcements

Calender#Calendar & Lecture Notes

Syllabus

(:*toc:)

There is no required text for the course. Notes will be posted online on the course webpage.

• 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.

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.

TA Contact: AE106, x2857,

TA Contact: AE106, x2857, anchup@rpi.edu

TA Office Hours: 12:00-1:50PM TF
TA Contact: AE106, x2857, anchup@rpi.edu

Class Time: MR 10-11:50AM
Room: Carnegie 113\\

TA: Pranay Anchuri
TA Office Hours: TBD
TA Contact:

Class: 10-11:50AM, MR, Room: Carnegie 113\\

TA & TA Office Hours: Pranay Anchuri, Hours TBD

Calendar & Lecture Notes

[l]Social Network Analysis (SNA)

[row bgcolor=aliceblue] [l]R: Dec 9 [l]EXAM III

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 R software. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of pmwiki markup usage will be your responsibility.

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.

CSCI-4390/6390: Data Mining, Fall 2010

Class: 10-11:50AM, MR, Room: TBD
Instructor Office Hours: 12-1PM, MR
TA & TA Office Hours: TBD

• Aug 2: Course website is up, with the tentative calendar and syllabus.

[l]M: Aug 30

[l]R: Sep 2

[l]M: Sep 6

[l]R: Sep 9

[l]M: Sep 13

[l]R: Sep 16

[l]M: Sep 20

[l]R: Sep 23

[l]M: Sep 27

[l]R: Sep 30

[l]M: Oct 4

[l]R: Oct 7

[l]Tue: Oct 12

[l]R: Oct 14

[l]M: Oct 18

[l]R: Oct 21

[l]M: Oct 25

[l]R: Oct 28

[l]M: Nov 1

[l]R: Nov 4

[l]M: Nov 8

[l]R: Nov 11

[l]M: Nov 15

[l]R: Nov 18

[l]M: Nov 22

[l]R: Nov 25

[l]M: Nov 29

[l]R: Dec 2

[l]M: Dec 6

[l]R: Dec 9

[l]PDF

[l]PDF

[l]PDF [l]Video

[l]PDF [l]Video

• Dec 4: Exam III solutions have been posted.

• Dec 2: Solutions to Assignment 6 posted on the assignment page

[l] PDF

[l]PDF

[l]PDF [l]PDF [l]Video

[l]PDF

[l]Kernel Methods: Kernel K-means

[l]PDF [l]Video

[l]Kernel Methods: Kernel PCA/LDA

[l]CLUS: Kernel K-means

[l]PDF [l]Video

[l]CLUS: Hierarchical/Density-based Clustering

[l]CLUS: Density-based Clustering (Kernel Density Estimation) [l]PDF

[l]PDF

[l]PDF

• Nov 17: Assignment 6 posted.

[l]CLUS: Subspace Clustering

[l]PDF [l]Video

• Nov 12: Exam II solutions have been posted.

• Nov 12: [((Attach:)exam2-sol.pdf | Exam II solutions]] have been posted.

[l]CLUS: Hierarchical/Density-based

[l]CLUS: Density-based (Kernel Density Estimation)

[l]PDF [l]Video

[l]PDF [l]Video

• Nov 4: Solutions to Assignment 5 posted on the assignment page.

[l]CLUS: Hierarchical

[l]CLUS: Density-based

[l]CLUS: Subspace

[l]CLUS: Spectral Clustering

[l]CLUS: Cluster Validity

[l]CLASS: Kernel PCA/LDA

[l]CLASS: Classifier Evaluation

[l]PDF [l]Video

• Oct 31: Solutions to Assignment 4 posted on the assignment page.

[l]CLASS: Kernel SVM, Rule-based

[l]PDF [l]Video

[l]PDF [l]PDF [l]Video

[l]PDF

[l]PDF

[l]PDF

[l]PDF

• Oct 24: Assignment 5 posted.

• Oct 13: Assignment 5 posted.

[l]PDF [l]Video

[l]PDF [l]Video

[l]CLASS: Support Vector Machines (SVM)

[l]CLASS: SVM contd.

[l]CLASS: Instance-based/Rule-based

[l]FPM:Sequence Mining, CLASS: Probabilistic

[l]CLASS: Instance-based/Rule-based

[l]Video

[l]PDF [l]Video

[l]PDF [l] Video

[l] [l] [l]

[l]CLUS: Hierarchical

[l]EXAM II

• Oct 13: Exam I solutions have been posted.

[l]PDF

[l]PDF

• Oct 10: Assignment 4 has been posted.

[l]PDF [l]Video

• Oct 4: Solutions for Assignment 3 posted.

[l]EDA: Linear Discriminant Analysis: LDA

[l]EDA: Dimensionality Reduction: PCA

[l]EDA: Dimensionality Reduction: PCA/SVD

[l]PDF [l]Video

[l]

[l]FPM: Itemset Summaries

[l]FPM: Sequence Mining

[l]CLASS: Instance-based/Rule-based

[l]CLASS: Probabilistic

[l]CLASS: Support Vector Machines (SVM)

[l]CLASS: SVM contd.

[l]PDF [l]PDF [l]Video

• Sep 27: Solutions for Assignments 1 and 2 have been posted on the respective pages.

• Sep 26: Assignment 3 is now available.

[l]PDF

[l]PDF

[l]PDF [l]Video

[l]PDF

[l]PDF

[l]PDF [l]Video

[l]PDF

[l]PDF

[l]PDF

[l]PDF

• Sep 18: Assignment 2 is now available.

[l]PDF

[l]PDF

[l]Clustering (CLUS): Partitional (KMeans, EM)

[l]PDF

[l]EDA: Numeric & Categorical Attributes

[!c]Chapters [!c]Lecture Notes

[l]

[l]PDF

PDF

PDF

PDF [l]Video

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]

[l]Video

[l]CLUS: Density-based

[l]M: Aug 31

[l]Tue: Oct 13

[table border=1 width=100%] [row bgcolor=lavender] [!c]Day: Date [!c]Topic [!c]Notes [!c]Video

[row] [l]M: Aug 31 [l]Data Mining Overview [l]PDF [l] [row bgcolor=aliceblue] [l]R: Sep 3 [l]Exploratory Data Analysis (EDA): Numeric Attributes [l]PDF [l]Video

[row] [l]M: Sep 7 [l]Labor Day Holiday [row bgcolor=aliceblue] [l]R: Sep 10 [l]EDA: Numeric & Categorical Attributes [l]PDF [l]Video

[row] [l]M: Sep 14 [l]Frequent Pattern Mining (FPM): Itemset Mining [l] [l] [row bgcolor=aliceblue] [l]R: Sep 17 [l]Clustering (CLUS): Partitional [l] [l]

[row] [l]M: Sep 21 [l]Classification (CLASS): Decision Trees [l] [l] [row bgcolor=aliceblue] [l]R: Sep 24 [l]EDA: High Dimensional Data [l] [l]

[row] [l]M: Sep 28 [l]EDA: Dimensionality Reduction (PCA/SVD) [l] [l] [row bgcolor=aliceblue] [l]R: Oct 1 [l]EDA: Linear Discriminant Analysis (LDA) [l] [l]

[row] [l]M: Oct 5 [l]EXAM I [row bgcolor=aliceblue] [l]R: Oct 8 [l]FPM: Itemset Summaries [l] [l]

[row] [l]T: Oct 13 (Monday Schedule) [l]FPM: Sequence Mining [l] [l] [row bgcolor=aliceblue] [l]R: Oct 15 [l]CLASS: Instance-based/Rule-based [l] [l]

[row] [l]M: Oct 19 [l]CLASS: Probabilistic [l] [l] [row bgcolor=aliceblue] [l]R: Oct 22 [l]CLASS: Support Vector Machines (SVM) [l] [l]

[row] [l]M: Oct 26 [l]CLASS: SVM contd. [l] [l] [row bgcolor=aliceblue] [l]R: Oct 29 [l]CLAS: Ensemble Methods [l] [l]

[row] [l]M: Nov 2 [l]EXAM II [row bgcolor=aliceblue] [l]R: Nov 5 [l]CLUS: Hierarchical [l] [l]

[row] [l]M: Nov 9 [l]CLUS: Density-based [l] [l] [row bgcolor=aliceblue] [l]R: Nov 12 [l]CLUS: Subspace [l] [l]

[row] [l]M: Nov 16 [l]CLUS: Subspace contd. [l] [l] [row bgcolor=aliceblue] [l]R: Nov 19 [l]CLASS: Kernel Methods (Kernel SVM) [l] [l]

[row] [l]M: Nov 23 [l]CLASS: Kernel PCA/LDA [l] [l] [row bgcolor=aliceblue] [l]R: Nov 26 [l]Thanksgiving Break

[row] [l]M: Nov 30 [l]CLUS: Spectral Clustering [l] [l] [row bgcolor=aliceblue] [l]R: Dec 3 [l]EXAM III

[row] [l]M: Dec 7 [l]Social Network Analysis (SNA) [l] [l] [row bgcolor=aliceblue] [l]R: Dec 10 [l]SNA: Graph Mining [l] [l]

[tableend]

(:cellnr bgcolor=aliceblue:) R: Sep 10 (:cell:) EDA: Numeric & Categorical Attributes

(:cellnr bgcolor=lavender:) Day: Date (:cell bgcolor=lavender:) Topic

(:cellnr:) M: Aug 31 (:cell:) Data Mining Overview

(:cellnr bgcolor=aliceblue:) R: Sep 3 (:cell:) Exploratory Data Analysis (EDA): Numeric Attributes

(:cellnr:) M: Sep 7 (:cell:) Labor Day Holiday

(:cellnr:) M: Sep 14 (:cell:) Frequent Pattern Mining (FPM): Itemset Mining

(:cellnr bgcolor=aliceblue:) R: Sep 17 (:cell:) Clustering (CLUS): Partitional

(:cellnr:) M: Sep 21 (:cell:) Classification (CLASS): Decision Trees

(:cellnr bgcolor=aliceblue:) R: Sep 24 (:cell:) EDA: High Dimensional Data

(:cellnr:) M: Sep 28 (:cell:) EDA: Dimensionality Reduction (PCA/SVD)

(:cellnr bgcolor=aliceblue:) R: Oct 1 (:cell:) EDA: Linear Discriminant Analysis (LDA)

(:cellnr:) M: Oct 5 (:cell:) EXAM I (:cellnr bgcolor=aliceblue:) R: Oct 8 (:cell:) FPM: Itemset Summaries

(:cellnr:) T: Oct 13 (Monday Schedule) (:cell:) FPM: Sequence Mining

(:cellnr bgcolor=aliceblue:) R: Oct 15 (:cell:) CLASS: Instance-based/Rule-based

(:cellnr:) M: Oct 19 (:cell:) CLASS: Probabilistic

(:cellnr bgcolor=aliceblue:) R: Oct 22 (:cell:) CLASS: Support Vector Machines (SVM)

(:cellnr:) M: Oct 26 (:cell:) CLASS: SVM contd.

(:cellnr bgcolor=aliceblue:) R: Oct 29 (:cell:) CLAS: Ensemble Methods

(:cellnr:) M: Nov 2 (:cell:) EXAM II (:cellnr bgcolor=aliceblue:) R: Nov 5 (:cell:) CLUS: Hierarchical

(:cellnr:) M: Nov 9 (:cell:) CLUS: Density-based

(:cellnr bgcolor=aliceblue:) R: Nov 12 (:cell:) CLUS: Subspace

(:cellnr:) M: Nov 16 (:cell:) CLUS: Subspace contd.

(:cellnr bgcolor=aliceblue:) R: Nov 19 (:cell:) CLASS: Kernel Methods (Kernel SVM)

(:cellnr:) M: Nov 23 (:cell:) CLASS: Kernel PCA/LDA

(:cellnr bgcolor=aliceblue:) R: Nov 26 (:cell:) Thanksgiving Break

(:cellnr:) M: Nov 30 (:cell:) CLUS: Spectral Clustering

(:cellnr bgcolor=aliceblue:) R: Dec 3 (:cell:) EXAM III

(:cellnr:) M: Dec 7 (:cell:) Social Network Analysis (SNA)

(:cellnr bgcolor=aliceblue:) R: Dec 10 (:cell:) SNA: Graph Mining

(:table border=1 width=100%:)

\\

(:table border=1 width=70% align=left:)

(:table border=1 width=80% align=center:)

(:table align=center border=1 width=80%:)

(:cell bgcolor=lavender:)Notes (:cell bgcolor=lavender:)Video

(:table align=center border=1 width=100%:) (:cellnr bgcolor=lavender:) Topic (:cell:)Notes (:cell:)Video

(:cellnr:) M: Aug 31 – Data Mining Overview (:cell:) PDF (:cell:) (:cellnr bgcolor=aliceblue:) R: Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes (:cell:) PDF (:cell:) Video

(:cellnr:) M: Sep 7 – Labor Day Holiday (:cellnr bgcolor=aliceblue:) R: Sep 10 – EDA: Numeric & Categorical Attributes (:cell:) PDF (:cell:) Video

(:cellnr:) M: Sep 14 – Frequent Pattern Mining (FPM): Itemset Mining (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Sep 17 – Clustering (CLUS): Partitional (:cell:) (:cell:)

(:cellnr:) M: Sep 21 – Classification (CLASS): Decision Trees (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Sep 24 – EDA: High Dimensional Data (:cell:) (:cell:)

(:cellnr:) M: Sep 28 – EDA: Dimensionality Reduction (PCA/SVD) (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Oct 1 – EDA: Linear Discriminant Analysis (LDA) (:cell:) (:cell:)

(:cellnr:) M: Oct 5– EXAM I (:cellnr bgcolor=aliceblue:) R: Oct 8 – FPM: Itemset Summaries (:cell:) (:cell:)

(:cellnr:) T: Oct 13 – (Monday Schedule) FPM: Sequence Mining (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Oct 15 – CLASS: Instance-based/Rule-based (:cell:) (:cell:)

(:cellnr:) M: Oct 19 – CLASS: Probabilistic (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Oct 22 – CLASS: Support Vector Machines (SVM) (:cell:) (:cell:)

(:cellnr:) M: Oct 26 – CLASS: SVM contd. (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Oct 29 – CLAS: Ensemble Methods (:cell:) (:cell:)

(:cellnr:) M: Nov 2 – EXAM II (:cellnr bgcolor=aliceblue:) R: Nov 5 – CLUS: Hierarchical (:cell:) (:cell:)

(:cellnr:) M: Nov 9 – CLUS: Density-based (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Nov 12 – CLUS: Subspace (:cell:) (:cell:)

(:cellnr:) M: Nov 16 – CLUS: Subspace contd. (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Nov 19 – CLASS: Kernel Methods (Kernel SVM) (:cell:) (:cell:)

(:cellnr:) M: Nov 23 – CLASS: Kernel PCA/LDA (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Nov 26 – Thanksgiving Break

(:cellnr:) M: Nov 30 - CLUS: Spectral Clustering (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Dec 3 – EXAM III

(:cellnr:) M: Dec 7 – Social Network Analysis (SNA) (:cell:) (:cell:) (:cellnr bgcolor=aliceblue:) R: Dec 10 - SNA: Graph Mining (:cell:) (:cell:)

(:tableend:)

• 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.

[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes(Notes(PDF))(Video)

[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes(Notes (PDF))(Video)

[l]Sep 10 – EDA: Numeric & Categorical Attributes (Notes (PDF))(Video)

[l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes (Notes (PDF)) (Video)

[l]Sep 10 – EDA: Numeric & Categorical Attributes (Notes (PDF))(Video)

[l]Sep 14 – Frequent Pattern Mining (FPM): Itemset Mining

[l]Sep 21 – Classification (CLASS): Decision Trees [l]Sep 24 – EDA: High Dimensional Data

[l]Sep 10 – EDA: Numeric & Categorical Attributes (Video)

[l]Sep 10 – EDA: Numeric & Categorical Attributes Video)

[l]Sep 14 – Frequent Pattern Mining (FPM): Itemset Mining [l]Sep 17 – Clustering (CLUS): Partitional

[l]Sep 21 – Classification (CLASS): Decision Trees [l]Sep 24 – EDA: High Dimensional Data

[l]Sep 28 – EDA: Dimensionality Reduction (PCA/SVD)

• Sep 8: Assignment 1 has been posted. See the general R/pmwiki instruction at Assignments and see the specific assignment at Assign1

[l]Aug 31 – Data Mining Overview: (PDF) [l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes (Video)

Calendar & Lecture Notes/Videos

[l]Aug 31 – Data Mining Overview: PDF [l]Sep 3 – Exploratory Data Analysis (EDA): Numeric Attributes Video

[table align=center border=1 width=100%]

Class: 10-11:50AM, MR, Low 3045\\

Calendar & Lecture Notes

[l]Aug 31 – Data Mining Overview

• Sep 2: Passwords for the assignment submission wiki were sent out yesterday. Contact me if you did not get the email.

CSCI-4390/6390: Data Mining, Fall 2009

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 – 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– EXAM I [l]Oct 8 – FPM: Itemset Summaries

[row] [l]Oct 13 – (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 – 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 – Thanksgiving Break

[row] [l]Nov 30 - CLUS: Spectral Clustering [l]Dec 3 – 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 R software. Students are expected to learn R on their own. Assignments must be submitted online at the wiki site. Knowledge of 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 Rensselaer Handbook of Student Rights and Responsibilities on this topic. (:divend:) (:tableend:)