CS 525 Knowledge Discovery and Data Mining

Syllabus— Fall 2009

Prof. Carolina Ruiz

COURSE DESCRIPTION:

Due to advances in technology and the availability of increasingly cheap storage devices, data in different domains have been accumulating at an impressively high rate in recent years, leading to very large databases. This course presents current research in Knowledge Discovery in Databases (KDD) dealing with the data integration, mining, and interpretation of patterns in such databases. Topics include data warehousing and mediation techniques aimed at integrating distributed, heterogeneous datasources; data mining techniques such as decision trees, association rule mining, and statistical analysis for discovery of patterns in the integrated data; and evaluation and interpretation of the mined patterns using visualization techniques. The work discussed originates in the fields of artificial intelligence, databases, information retrieval, data visualization, and statistics. Industrial and scientific applications will be given.

Students will be expected to read assigned textbook chapters and research papers, and work on implementation/research projects that cover the different stages of the KDD process.

This course can be used to satisfy the graduate AI bin requirement.

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CLASS MEETING:

Time: Tuesdays and Thursdays 2:00-3:20 pm
Room: WB323

Students are also encouraged to attend the Knowledge Discovery in Databases and Data Mining Research Group (KDDRG) Seminar Fridays at 1 pm in Beckett Conference Room (FL246).

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INSTRUCTOR:

Prof. Carolina Ruiz

Office: FL 232
Phone Number: (508) 831-5640
Office Hours: Mondays 11-12 noon, Thursdays 1-1:50 pm, or by appointment.

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TEXTBOOK:

• Required:
  "Data Mining (Second Edition)". I.H. Witten and E. Frank. Morgan Kaufmann Publishers. 2005. ISBN: 0-12-088407-0
• Recommended: Several other books on the subject and related subjects are recommended below. Some research papers will be handed out during the term.

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PREREQUISITE:

Background in artificial intelligence, databases, and statistics at the undergraduate level, or permission of the instructor. Proficiency in a high level programming language (preferable Java) is required.

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GRADES:

6 Projects (15% each project)	90%
Class Participation	10%

Your final grade will reflect your own work and achievements during the course. Any type of cheating will be penalized and reported to the WPI Judicial Board in accordance with the Academic Honesty Policy.

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CLASS PARTICIPATION

All students are expected to read the material assigned for each class in advance and to participate in class discussions. Also, students will take turns presenting papers and leading class discussions of assigned readings. Class participation will be taken into account when deciding students' final grades.

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PROJECTS AND ASSIGNMENTS

There will be a total of six projects related to the data mining stages and/or techniques covered in the class. Datasets for those projects will be selected from online database repositories, or other sources.

About the Weka System: For most of the projects, we will use the Weka system (http://www.cs.waikato.ac.nz/ml/weka/). Weka is an excellent data-mining environment. It provides a large collection of Java-based mining algorithms, data preprocessing filters, and experimentation capabilities. Weka is open source software issued under the GNU General Public License. For more information on the Weka system, to download the system and to get its documentation, look at Weka's webpage (http://www.cs.waikato.ac.nz/ml/weka/). You should download and use the latest developer version of the system (currently 3-7-0).

• Project 1: Data & Data Preparation
• Project 2: Classification
• Project 3: Numeric Prediction
• Project 4: Association Analysis
• Project 5: Cluster Analysis
• Project 6: Anomaly Detection and Advanced Data Mining Applications

Students will be required to provide both a written report and an oral (in-class) presentation describing their achievements in each of these projects.

More detailed descriptions of the assignments and projects will be posted to the course webpage at the appropriate times during the semester.

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CLASS MAILING LIST

The mailing list for this class is:

This mailing list reaches the professor and all the students in the class.

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CLASS WEB PAGES

The webpages for this class are located at http://www.cs.wpi.edu/~cs525d/f09/
Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email and the class web pages frequently.

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WARNING:

Small changes to this syllabus may be made during the course of the semester.

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ADDITIONAL SUGGESTED REFERENCES

Knowledge Discovery and Data Mining

• "Data Mining: Concepts and Techniques". J. Han and M. Kamber. Morgan Kaufmann Publishers. 2001. ISBN: 1-55860-489-8.
• "Advances in Knowledge Discovery and Data Mining". Eds.: Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy. The MIT Press, 1995.
• "Data Mining. Technologies, Techniques, Tools, and Trends". B. Thuraisingham. CRC, 1998.
• "Data Mining. A hands-on approach for business professionals". R. Groth. Prentice Hall, 1998.
• "Data Preparation for Data Mining". Dorian Pyle, 3/99.
• "Data Mining". P. Adriaans & D. Zantinge
• "Data Mining Methods for Knowledge Discovery" Cios, Pedrycz, & Swiniarski, 1998.
• "Data Mining Techniques for Marketing, Sales and Customer Support". Berry & Linoff.
• "Decision Support using Data Mining". Anand and Buchner.
• "Feature Selection for Knowledge Discovery and Data Mining". Liu and Motoda.
• "Feature Extraction, Construction and Selection: A Data Mining Perpective". Eds: Motoda and Liu.
• "Introduction to Data Mining". Tan, Steinbach, & Kumar. 2006.
• "The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data". Ronen Feldman, James Sanger. 2006.
• "Knowledge Acquisition from Databases". Xindong Wu.
• "Mining Very Large Databases with Parallel Processing". Alex Freitas, Simon Lavington.
• "Predictive Data-Mining: A Practical Guide". Weiss & Indurkhya.
• "Machine Learning and Data Mining: Methods and Applications." Michalski, Bratko, and Kubat, 1998; John Wiley & Sons.
• "Rough Sets and Data Mining: Analysis of Imprecise Data." Eds: Lin and Cercone; Kluwer.
• "Seven Methods for Transforming Corporate Data into Business Intelligence". Vasant Dhar and Roger Stein; Prentice-Hall, 1997.

Databases

• "A First Course in Database Systems". J. Ullman, J. Widom. Prentice-Hall, 1997.
• "Database Management Systems", 2nd ed. R. Ramakrishnan. McGraw-Hill, 1999.
• Advanced Database Systems". C. Zaniolo, S. Ceri, C. Faloutsos, R.T. Snodgrass, V.S. Subrahmanian, R. Zicari. The Morgan Kaufmann, 1997.
• "Readings in Database Systems". 2nd Edition. Ed. M. Stonebraker. 1994, Morgan Kaufmann.

Statistics

• "Statistical Inference for Management and Economics". P. Billingsley, D. Croft, D. Huntsberger, C. Watson. Boston: Allyn and Bacon, Inc. 1986.
• "Probability and Statistics". 2nd edition. M. DeGroot. Addison Wesley, 1986.
• "Statistical Inference". G. Casella, R. Berger. Wadsworth and Brooks/Cole, 1990.

Machine Learning

• "Machine Learning". Tom M. Mitchell. McGraw-Hill, 1997.
• "Elements of Machine Learning". P. Langley. Morgan Kaufmann Publishers, Inc. 1996.

General AI

• "Artificial Intelligence: A Modern Approach". S. Russell, P. Norvig. Prentice Hall, 1995. ISBN 0-13-103805-2
• "Artificial Intelligence: Theory and Practice". T. Dean, J. Allen, Y. Aloimonos. The Benjamin/Cummings Publishing Company, Inc. 1995.

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OTHER ONLINE RESOURCES:

Previous offerings of CS525d Knowledge Discovery and Data Mining

Webpages of my previous offerings of this course:

• Spring 2008
• Spring 2004
• Fall 2001
• Fall 99

Previous offerings of CS4445

Webpages of my previous offerings of the undergraduate data mining course contain plenty of useful resources: practice exams, exams, homework, solutions of those exams/hw, etc.

• A Term 2008
• B Term 2006
• A Term 2004
• D Term 2004
• D Term 2003

Data Sets

• Time Series Data Library
• Data Repositories
• Datasets for Data Mining
• CMU's StatLib-Datasets Archive
• Data Mining and Knowledge Discovery - Data Sources
• Miscellaneous
• Intrusion Detection Data - Sequences

KDD

• ACM SIGKDD.
• KDNuggets: Data Mining and Knowledge Discovery Resources
• Data Mining and Knowledge Discovery
• RPI's Data Mining Links
• KDD Research Groups
  • Data Miners - Dept. of Computer Science. Univ. of Helsinki

KDD Commercial Products / Prototypes

• IBM's Intelligent Miner
• SGI's MineSet
• SFU's DBMiner

Data Warehousing and OLAP

• Data Warehousing and OLAP - A Research-Oriented Bibliography
• OLAP Council White Paper

Statistics

• CMU's StatLib
• Universiteit van Amsterdam

Machine Learning

• Online Machine Learning Resources
• Machine Learning Resources
• Machine Learning Papers
• UCI Machine Learning
• Reinforcement Learning and Friends at Carnegie Mellon
• A Bibliography on Automatic Text Categorization

General AI

• CMU Artificial Intelligence Repository
• AI Journals
• Challenge Problems for Artificial Intelligence

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