CAS CS 565, Data Mining Fall 2011


Mon-Wed 2:30-4:00 pm

Course Outline

Data mining is the analysis of (often large) observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data analyst (Hand, Mannila and Smyth: Principles of Data Mining)

The goal of this course is to provide an introduction to the main topics in data mining including: frequent-itemset mining, clustering, classification, link-analysis ranking, dimensionality reduction etc. The focus of the course will be on the algorithmic issues as well as applications of data mining to real-world problems. Students will be required to solve small written and programming assignments that will help them better understand the covered material.


Evimaria Terzi,
Office Hours: Tues 11:00 am - 12:30 pm and Wed 4:00 pm -5:30 pm or by appointment.


  1. Three programming projects

  2. Three problem sets

  3. Two exams; one midterm and one final


Working knowledge of programming and data structures (CS 112, or equivalent). Familiarity with basic algorithmic concepts, probability, statistics and linear algebra. Programming projects will require knowledge of C (or C), java or python.


Many of these slides have been borrowed by lectures, talks and tutorials prepared by: Aris Anagnostopoulos, Petros Drineas, Piotr Indyk, George Kollios, Heikki Mannila, Panayiotis Tsaparas, Jeffrey Ullman.

The lectures on classification are using the slides from the book ‘‘Introduction to Data Mining" by Tan, Steinbach, Kumar, available here.

Sept 7 No class
Sept 12 What is datamining/ Introductory lecture .pptx,.pdf
Sept 14 Mining frequent itemsets and association rules .pptx,.pdf
Sept 16 Homework 1; due Oct 3 download
Sept 19, 21 Condensed representations of itemset collections .pptx,.pdf
Sept 26 Covering Problems .pptx,.pdf
Sept 28 Clustering .pptx,.pdf
Oct 3 Clustering and Hierarchical Clustering .pptx,.pdf
Oct 4 Project 1; due Oct 17 download
Oct 5 Clustering Aggregation .pptx,.pdf
Oct 8 Homework 2; due Oct 24 download
Oct 10 Holiday
Oct 12 Co-clustering and Density-based clustering .pptx,.pdf
Oct 17 Dimensionality Reduction .pptx,.pdf
Oct 17 Project 1; due Oct 31 download
Oct 19 Dimensionality Reduction .pptx,.pdf
Oct 24 Midterm
Oct 26 Classification and Decision Trees .pptx,.pdf
Oct 31 k-NN and Naive Bayes Classification .pptx,.pdf
Nov 2 Support Vector Machines .pptx,.pdf
Nov 7 Evaluation and Ensembles .pptx,.pdf
Nov 7 Project 3; due Nov 28 download
Nov 9 Link Analysis Ranking .pptx,.pdf
Nov 14 An extra class on weka
Nov 16 PageRank and Personalized PageRank .pptx,.pdf
Nov 21 Voting Systems
Nov 23 Thanksgiving break
Nov 27 Homework 3; due Dec 14 download
Nov 28, 30 Graph Clustering .pptx,.pdf
Dec 5 Privacy Preserving Graph Mininig .pptx,.pdf
Dec 7 Time-series analysis .pptx,.pdf
Week starting Dec 16 Final Exam

Reading material

Mining frequent itemsets and association rules

Rakesh Agrawal and Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules. In International Conference on Very Large Databases, VLDB 1994.

Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, and A. Inkeri Verkamo: Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, 307 - 328. AAAI Press, 1996.

Roberto J. Bayardo Jr.: Efficiently Mining Long Patterns from Databases. In SIGMOD Conference 1998: 85-93

F. Afrati, A. Gionis, H. Mannila: Approximating a Collection of Frequent Sets. In International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2004.


A. Gionis, H. Mannila, P. Tsaparas: Clustering Aggregation. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2006.

Jon Kleinberg: An impossibility theorem for clustering. In NIPS, 2002.

A. Anagnostopoulos, A. Dasgupta, R. Kumar: Approximation algorithms for co-clustering. In PODS, 2008.

Kai Puolamaki, Sami Hanhijarvi, Gemma C. Garriga: An approximation ratio for biclustering. In Information Processing Letters, 2008.

Rakesh Agrawal , Johannes Gehrke , Dimitrios Gunopulos , Prabhakar Raghavan: Automatic subspace clustering for high-dimensional data. In SIGMOD, 1998.

Dimensionality Reduction

Christos Faloutsos, King-Ip Lin: FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In SIGMOD 1995.

Dimitris Achlioptas: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. In Journal of Comp. & Sys. Sci.,special issue of invited papers from PODS’01.

Ella Bingham, Heikki Mannila: Random projection in dimensionality reduction: Applications to image and text data. In KDD, 2001.

For MDS read: Programming Collective Intelligence: Building Smart Web 2.0 Applications, Chapter 3, pages 49-52.

For Petros Drineas’ tutorials on linear-algebra methods for data mining see here.

For Piotr Indyk’s tutorial on Nearest-Neighbor search in low and high dimensions see here and here.

Similarity and Nearest-Neighbor (NN) search

Taher H. Haveliwala, Aristides Gionis, Dan Klein, Piotr Indyk: Evaluating strategies for similarity search on the web. In WWW Conference 2002.

Aristides Gionis, Piotr Indyk, Rajeev Motwani:Similarity Search in High Dimensions via Hashing. In VLDB 1999.

Link Analysis Ranking (LAR)

J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998.

S. Brin, R. Motwani, L. Page and T. Winograd: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford Digital Libraries, 1998.

A. Borodin, J. S. Rosenthal, G. O. Roberts, P. Tsaparas: Link Analysis Ranking: Algorithms, Theory and Experimets. ACM Transactions on Internet Technologies (TOIT), Vol 5, No 1, February 2005.

Cuts on Graphs

David Cheng, Ravi Kannan, Santosh Vempala and Grant Wang:A Divide-and- Merge methodology for Clustering In ACM SIGMOD/PODS, 2005.

Ravi Kannan and Santosh Vempala and Andrian Vetta: On Clusterings: Good, Bad and Spectral. In the Journal of the ACM, May 2004.

Time-series segmentation

E. Terzi and P. Tsaparas: Efficient algorithms for sequence segmentation. In Siam Data Mining Conference (SDM), 2006.


There is no required textbook for the course. Suggested textbooks are:

Grading Policy

Incompletes will not be given

Late Assignment Policy: There will be a penalty of 10% per day, up to three days late. After that no credit will be given.

Collaborations/Academic Honesty

All course participants must adhere to the CAS Academic Conduct Code. All instances of adacemic dishonesty will be reported to the academic conduct committee