Mondays and Wednesdays 11:00AM-12:15PM; Rm. MSB 276
Office Hours: Mondays and Wednesdays 10:00-11:00AM
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CS 4/56101 Algorithms
CS 33001 Data Structures
Or Consent of the Instructor
This course teaches the fundamental concepts and techniques of data mining. We will cover a set of interesting topics, including association rule mining, clustering, classification, dimension reduction, mining complex structures, web mining, etc.
Each student will be expected to present a paper and lead the discussion following his/her presentation and do a project on selected topics.
There will be neither homework nor exam.
P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2005.
Other references:
[1] Data Mining --- Concepts and techniques, by Han and Kamber,
Morgan Kaufmann, 2001. (ISBN:1-55860-489-8)
[2] Principles of Data Mining, by Hand, Mannila, and
Smyth, MIT Press, 2001. (ISBN:0-262-08290-X)
[3] The Elements of Statistical Learning --- Data Mining, Inference, and
Prediction, by Hastie, Tibshirani, and Friedman,
Springer, 2001. (ISBN:0-387-95284-5)
[4] Mining the Web --- Discovering Knowledge from Hypertext Data, by Chakrabarti, Morgan Kaufmann, 2003. (ISBN:1-55860-754-4)
[5] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005.
Additional materials will include papers supplied by the instructor
Requirements & Grading Policy
A student's grade will be determined as a weighted average of project (50%), presentation (35%), and class participation (15%).
Updated:
Ø The Paper Presentation Assignment is available. First draft due on Oct. 14th.
Ø Lecture 4 on Classification.
Ø Recommended Reading List for Lecture 3, 4 and 5 is available.
Ø Lecture 5 on Anomaly Detection .
Ø Lecture 6 on Intrusion Detection.
Ø Lecture 7 on Web Mining.
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