CSE 591 Data Mining

Instructor: Huan Liu

=============== LEARNING, INNOVATING via PRACTICE =============

COURSE AND OBJECTIVES



The course is at the graduate level and most topics are on-going research
work. It is a seminar course and active class participation is
expected. It is designed to encourage students to actively learn advanced
concepts, to independently think over research and development issues,
to pro-actively relate what we learn to the real problems in practice,
to stimulate and brain-storm new ideas, to intelligently solve pressing
problems in various phases of data mining, and to create and implement
systems or components that can be used and re-used for data mining.

You're welcome to contribute papers of significance and interest from your 
perspective. 



SCHEDULE (tentative)

C1 Project Presentation and Discussion
C2 Project Presentation and Discussion
C3 Project Presentation and Discussion
C4 Project Presentation and Discussion



Classification
--------------
  Quinlan86
     J.R. Quinlan, ``Induction of Decision Trees'', Machine Learning, 1:81-106
        1986

  Domingos&Pazzani96
     P. Domingos and M. Pazzani, ``Beyond Independence: Conditions for
       the Optimality of the Simple Bayesian Classifier'', Proceedings of
       the 13th International Conference on Machine Learning. 1996. 105-112.
     ps.gz

  Lu et al95
     H. Lu, R.Setiono and H. Liu, ``NeuroRule: A Connectionist Approach to
       Data Mining'', International Conference on Very Large Databases
       (VLDB'95), Oct. 1995, Zurich, Switzland.
     ps

  Domingos99
     P. Domingo, ``MetaCost: A General Method for Making Classifiers 
       Cost-Sensitive''. Proceedings of the Fifth International Conference 
       on Knowledge Discovery and Data Mining (pp. 155-164), 1999. 
       San Diego, CA.        
     ps.gz

Evaluation
----------
  Witten&Frank00
     Witten, I.H. and Frank, E. ``Data Mining - Practical Machine
     Learning Tools and Techniques with JAVA Implementations'',
     Morgan Kaufmann, 2000.

  Lim et al99
     Lim, T.-S., Loh, W.-Y. and Shih, Y.-S. ``A Comparison of Prediction
       Accuracy, Complexity, and Training Time of Thirty-three Old and
       New Classification Algorithms'', Machine Learning. Forthcoming.
       (Appendix containing complete tables of error rates, ranks, and
       training times; download the data sets in C4.5 (ASCII) format)
     link

Preprocessing
-------------
  Dash-Liu97
     M. Dash and H. Liu, ``Feature Selection for Classification''.
       Intelligent Data Analysis - An International Journal,
       Elsevier, Vol. 1, No. 3, 1997
       ps

  Hussain et al99
     F. Hussain, H. Liu, C.L. Tan, and M. Dash, ``Discretization:
       An Enabling Technique'', Technical Report, TRC6/99,
       School of Computing, National University of Singapore, 1999.
       link

  Gu et al00
     B. Gu, F. Hu, and H. Liu, ``Sampling and Its Application to
     Data Mining: A Survey'', Technical Report, TRA6/00,
     School of Computing, National University of Singapore, 2000.
     link

  Liu-etal01
     Database Selection 
   ps

Clustering
----------

 Meila-Heckerman98
    M. Meila and D. Heckerman, ``An Experimental Comparison of Several
      Clustering and Initialization Methods'', Technical Report,
      MSR-TR-98-06, Microsoft Research, Redmond, WA..
    ps

 Fisher96
    D.H. Fisher, ``Iterative Optimization and Simplification of Hierarchical
      Clusterings'', JAIR, 4, 147-178, 1996.
    ps

 Zhang-etal96
    T. Zhang, R. Ramakrishnan, and M. Livny, ``BIRCH: An Efficient Data
     Clustering Method for Very Large Databases'', SIGMOD96, 103-114.

 Ester et al98
    DBSCAN
    html

 Blum-Mitchell98
    A. Blum and T. Mitchell, ``Combining Labeled and Unlabeled Data with
     Co-Training'', Proceedings of the 11th Annual Conference on 
     Computational Learning Theory, pages 92--100, 1998.
    ps.gz

Association
-----------
  Agrawal-Srikantl94
    R. Agrawal and R. Srikant, ``Fast Algorithms for Mining Association
      Rules'', Proceedings International Conference on Very Large
      Data Bases, Santiago, Chile, September, 1994, 487-499.
    ps

 Han-Fu95
    J. Han and Y. Fu. ``Discovery of Multiple-level Association Rules from
       Large databases''. Proceedings International Conference on Very Large
       Data Bases, Zurich, Switzerland, September 1995, 420-431.
     ps

 Han-etal00
    J. Han, J. Pei, and Y. Yin, `` Mining Frequent Patterns without Candidate 
      Generation '', Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data 
      (SIGMOD'00). 
     link 


  Agrawal etal96
     R. Agrawal and J.C. Shafer, ``Parallel Mining of Association Rules''
       IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6,
       December 1996.
     ps

  Han etal97
     E-H. Han, G. Karypis and V. Kumar, ``Scalable Parallel Data Mining for
       Association Rules''. Proceedings ACM SIGMOD International Conference on
       Management of Data, May 1997.
     ps

Real-World Applications
----------------------
  Ng etal98
     K.S. Ng, H. Liu, and H.B. Kwah, ``A Data Mining Application:
       Customer Retention at the Port of Singapore Authority (PSA)'',
       SIGMOD'98, Industrial Track, June 1-4, 1998. Seattle, Washington, USA

Data Warehousing
----------------

  Garcia-Molina99
     H. Garcia-Molina, W. J. Labio, J. L. Wiener, Y. Zhuge. ``Distributed and
       Parallel Computing Issues in Data Warehousing'' (invited talk). To
      appear in Proceedings of ACM Principles of Distributed Computing
      Conference, 1999.
       link

 Widom95
    J.Widom, ``Research Problems in Data Warehousing'', Proceedings 4th
      International Conference on Information and Knowledge Management,
      Baltimore, Maryland, Nov. 1995, 25-30.
      link   

Anahory-Murray
    Data Warehousing in the Real World - A Practical Guide for
    Building Decision Support Systems

Web Data and Mining
-------------------

 Deutsch etal99, ``A Query Language for XML'', Proceedings of the
      Eighth International World Wide Web Conference (WWW8), 1999
      ps

 Candan etal01,``Resource Description Framework: Metadata and Its 
   Applications'', SIGKDD Explorations, July 2001. 3(1):6-19. 
   Abstract and Link

 Mutsumi00, RDF Resources
      Lass97,  Introduction RDF Metadata,
           html, 1997
      Bern98a, What the Semantic Web can represent
           html, 1998
      Bern98b, Why RDF model is different from the XML
           html, 1998

 Brin98, ``Extracting Patterns and Relations from the World-Wide
      Web'', WebDB Workshop at EDBT, 1998
      ps

 Brin-Page, ``Dynamic Data Mining'',
      ps

 Brin-Page98, ``The Anatomy of a Large-Scale Hypertextual Web Search
      Engine'', WWW7 / Computer Networks 30(1-7): 107-117 (1998)
      htm

 Kosala-Blockeel00, ``Web Mining Research: A Survey", SIGKDD
       Explorations, Volume 2, Issue 1: 1-15, 2000.
       link




PROJECT TOPICS

  The following list of topics is suggested for your reference. Students
can choose to work on one topic or to attack other big problems suggested
by the students after discussing with the lecturer.

o Image Mining
o Data Streaming
o Web Mining
o Data Selection (Focusing)
o Machine Learning (Co-training, ...)
o Scalable DM algorithms (Parallel Processing, Data Swarms)
o ... 

Other paper links

o http://www.cs.rpi.edu/~zaki/dmcourse/papers.html
o ...



Latest update: Jan 30, 2002