Main
CSCI4390/6390: Data Mining, Fall 2012
Class Time: MR 1011:50AM
Room: Greene 120
Instructor Office Hours: MR 121PM, Lally 307
TA: Nilothpal Talukder
TA Office Hours: W 24PM, Amos Eaton 119
TA Contact:
Table of Contents (hide)
Announcements

Calendar & Lecture Notes
A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.
Day: Date  Topic  Readings  Lectures


M: Aug 27  NO CLASS  
R: Aug 30  NO CLASS  
M: Sep 3  Labor Day Holiday  
R: Sep 6  Data Mining and Analysis (DA): Algebraic and Probabilistic Views  Attach:chap1.pdf  Attach:dmintro.pptx, Attach:Lecture1.PDF 
M: Sep 10  DA: Numeric Attributes  Attach:chap2.pdf  Attach:Lecture2.PDF 
R: Sep 13  DA: Numeric Attributes: Eigendecomposition  Attach:Lecture3.PDF  
M: Sep 17  DA: Dimensionality Reduction  Attach:chap7.pdf  Attach:Lecture4.PDF 
R: Sep 20  DA: High Dimensional Analysis  Attach:chap6.pdf  Attach:Lecture5.PDF 
M: Sep 24  DA: Categorical Data &  Attach:chap3.pdf  Attach:Lecture6.PDF 
R: Sep 27  DA: Kernel Methods  Attach:chap5.pdf  Attach:Lecture7.PDF 
M: Oct 1  DA: Kernels  Attach:Lecture8.PDF  
R: Oct 4  EXAM I  
Tue: Oct 9  Classification (CLASS): Linear Discriminants, SVMs  Attach:chap22.pdf  Attach:Lecture9.PDF 
R: Oct 11  CLASS: SVMs  Attach:chap23.pdf  Attach:Lecture10.PDF 
M: Oct 15  CLASS: Bayesian Classifier, Decision Trees  Attach:chap21.pdf, Attach:chap19.pdf  Attach:Lecture11.PDF 
R: Oct 18  CLASS: Classifier Evaluation  Attach:chap24.pdf  Attach:Lecture12.PDF 
M: Oct 22  CLASS: Classifier Evaluation  Attach:Lecture13.PDF  
R: Oct 25  Clustering (CLUS): Partitional  Attach:chap13.pdf  Attach:Lecture14.PDF 
M: Oct 29  NO CLASS  
R: Nov 1  EXAM II  
M: Nov 5  CLUS: EMbased  Attach:Lecture15.PDF  
R: Nov 8  CLUS: Hierarchical, Densitybased Clustering  Attach:chap14.pdf, Attach:chap15.pdf  Attach:Lecture16.PDF 
M: Nov 12  CLUS: Spectral & Graph Clustering  Attach:chap17.pdf  Attach:Lecture17.PDF 
R: Nov 15  CLUS: Spectral & Graph Clustering  Attach:Lecture18.PDF  
M: Nov 19  CLUS: Evaluation & Assessment  Attach:chap18.pdf  Attach:Lecture19.PDF 
R: Nov 22  Thanksgiving Break  
M: Nov 26  Frequent Pattern Mining (FPM): Itemset Mining  Attach:chap8.pdf, Attach:chap9.pdf  Attach:Lecture20.PDF 
R: Nov 29  FPM: Sequence Mining  Attach:chap10.pdf  Attach:Lecture21.PDF 
M: Dec 3  FPM: Graph Mining  Attach:chap11.pdf  Attach:Lecture22.PDF 
R: Dec 6  EXAM III 
Syllabus
IntroductionData 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. Learning ObjectivesAfter taking this course students will be
PrerequisitesThe prerequisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also essentially prerequisites, 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. You are expected to learn python on your own via web tutorials, etc. Assignments must be submitted via email to . TextbookStudents will be given draft chapters from the forthcoming book
The following text books are also good references:
Grading PolicyYour grade will be a combination of the following items.
Other Policies
Academic IntegrityYou 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. 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. 