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CS 685 Special Topics in Data mining

CS 685 Special Topics in Data mining. Instructor: Jinze Liu Spring 2008. Welcome!. Instructor: Jinze Liu Homepage: http://www.cs.uky.edu/~liuj Office: 237 Hardymon Building Email: liuj@cs.uky.edu Office hour: by appointment. Overview. Time: 2:00-3:15PM Tuesday and Thursday

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CS 685 Special Topics in Data mining

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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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