Course Related Readings

  1. Surajit Chaudhuri and Umesh Dayal, An Overview of Data Warehousing and OLAP Technology, ACM SIGMOD Record 26(1), March 1997
  2. M-S Chen, J. Han, & P. Yu, Data Mining: An Overview from a Database Perpective, IEEE TKDE, 8(6), 1996, pp. 866-883
  3. R. Agrawal, T. Imielinski, A. Swami: Mining Associations between Sets of Items in Massive Databases, Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Washington D.C., May 1993, 207-216.
  4. R. Agrawal, R. Srikant: Fast Algorithms for Mining Association Rules, Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, Sept. 1994.
  5. Jong Son Park, Ming-Syan Chen , and Philip S. Yu: An Effective Hash-Based Algorithm for Mining Association Rules, SIGMOD'95
  6. Ashoka Savasere, Edward Omiecinski and Shamkant B. Navathe: An Efficient Algorithm for Mining Association Rules in Large Databases, Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland, pp. 432-444
  7. Hannu Toivonen, Sampling Large Databases for Association Rules, Proceedings of 22th International Conference on Very Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India, pp. 134-145
  8. Ramakrishnan Srikant, Rakesh Agrawal:  Mining Generalized Association Rules. Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland, pp. 407-419
  9. Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland420-431
    11. Thomas Dietterich and Ghulum Bakiri, Solving Multiclass Learning Problems via Error-Correcting Outout Codes, Journal of Artificial Intelligence Research 2: 263-286.
  11. Yoav Freund and Robert Schapire, A Short Introduction to Boosting, Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999. (Appearing in Japanese, translation by Naoki Abe.)
  12. Robert Schapire, A Brief Introduction to Boosting, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
  13. Tom Dietterich Ensemble Learning Methods, In Michael A. Arbib (Ed.) Handbook of Brain Theory and Neural Networks, 2nd Edition, MIT Press.
  14. Manish Mehta, Rakesh Agrawal, and Jorma Rissanen. SLIQ: A fast scalable classifier for data mining. In Proc. of the Fifth Int'l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996.
  15. John C. Shafer, Rakesh Agrawal, and Manish Mehta. SPRINT: A Scalable Parallel Classifier for Data Mining. In Proc. 22nd Int. Conf. Very Large Databases (VLDB), 1996.
  16. R. Kohavi and B. Becker and D. Sommerfield. Improving Simple Bayes. Proceedings of the European Conference on Machine Learning (ECML), 1997.
  17. David D. Lewis. Naive Bayes at Forty: The Independence Assumption in Information Retrieval. Proceedings of the European Conference on Machine Learning (ECML), 1998.
  18. B. Schölkopf, C. J. C. Burges, and A. J. Smola. Introduction to Support Vector Learning. Chapter 1 of Advances in Kernel Methods --- Support Vector Learning. MIT Press, Cambridge, MA, 1999.