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Special Topics in Data Mining - Spring 2008

This course, taught by Jinze Liu, offers an in-depth exploration of Data Mining concepts, techniques, and applications. Scheduled for Tuesdays and Thursdays from 2:00-3:15 PM, the course includes student-led paper presentations, a research project, and class participation. Students will discuss various data mining methods including clustering, outlier detection, and association rules. Resources include seminal texts and recent papers in the field. No prerequisites are required, making it an ideal opportunity for students interested in AI, machine learning, and data analysis.

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Special Topics in Data Mining - Spring 2008

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  1. CS 685 Special Topics in Data mining Instructor: Jinze Liu Spring 2008

  2. Welcome! • Instructor: Jinze Liu • Homepage: http://www.cs.uky.edu/~liuj • Office: 237 Hardymon Building • Email: liuj@cs.uky.edu • Office hour: by appointment

  3. Overview • Time: 2:00-3:15PM Tuesday and Thursday • Place: POT 145 • Credit: 3 • Prerequisite: none • Preferred: Database, AI, Machine Learning, Statistics, Algorithms

  4. Overview • Textbook: none • A collection of papers in recent conferences and journals • References • Data Mining --- Concepts and techniques, by Han and Kamber, Morgan Kaufmann, 2006. (ISBN:1-55860-901-6) • Introduction to Data Mining, by Tan, Steinbach, and Kumar, Addison Wesley, 2006. (ISBN:0-321-32136-7) • Principles of Data Mining, by Hand, Mannila, and Smyth, MIT Press, 2001. (ISBN:0-262-08290-X) • The Elements of Statistical Learning --- Data Mining, Inference, and Prediction, by Hastie, Tibshirani, and Friedman, Springer, 2001. (ISBN:0-387-95284-5) • Mining the Web --- Discovering Knowledge from Hypertext Data, by Chakrabarti, Morgan Kaufmann, 2003. (ISBN:1-55860-754-4)

  5. Overview • Grading scheme • No homework • No exam

  6. Overview • Paper presentation • One per student • Research paper(s) • List of recommendations (will be available) • Your own pick (upon approval) • Three parts • Motivation for the research • Review of data mining methods • Discussion • Questions and comments from audience • Class participation: One question/comment per student • Order of presentation: will be arranged according to the topics • Please send in your choice of paper(s) by Jan 29th.

  7. Overview • Project (due May 1st) • One project: Individual or team project • Some suggestion will be available shortly • You are welcome to propose your own especially you have a dataset for analysis. • Due Feb 7th • Proposal: title and goal • Survey of related work: pros and cons • Outline of approach • Due April 1st • Implementation update • Due May 1st • Implementation • Evaluation • Discussion and future directions

  8. Topics • Scope:Data Mining • Topics: • Association Rule • Sequential Patterns • Graph Mining • Clustering and Outlier Detection • Classification and Prediction • Regression • Pattern Interestingness • Dimensionality Reduction • …

  9. Topics • Applications • Biomedical informatics • Bioinformatics • Web mining • Text mining • Graphics • Visualization • Financial data analysis • Intrusion detection • …

  10. KDD References • Data mining and KDD (SIGKDD: CDROM) • Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. • Journal: Data Mining and Knowledge Discovery, KDD Explorations • Database systems (SIGMOD: CD ROM) • Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA • Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc. • AI & Machine Learning • Conferences: Machine learning (ICML), AAAI, IJCAI, COLT (Learning Theory), etc. • Journals: Machine Learning, Artificial Intelligence, etc.

  11. KDD References • Statistics • Conferences: Joint Stat. Meeting, etc. • Journals: Annals of statistics, etc. • Bioinformatics • Conferences: ISMB, RECOMB, PSB, CSB, BIBE, etc. • Journals: J. of Computational Biology, Bioinformatics, etc. • Visualization • Conference proceedings: InfoVis, CHI, ACM-SIGGraph, etc. • Journals: IEEE Trans. visualization and computer graphics, etc.

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