Reading List:

Choose one of the papers listed below and email me your selection. Please note that you can NOT choose the papers in red color as they have already been selected by others.

Data Warehousing

• Efficient Computation of Iceberg Cubes with Complex Measures, Proc. 2001 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'01), Santa Barbara, CA, May 2001.(Chosen by Kevin Wang)

Feature Selection

• A Probabilistic Approach to Feature Selection - A Filter Solution, Huan Liu and Rudy Setiono, Proceedings of the 13th International Conference on Machine Learning (ICML'96), 1996.
• Greedy Attribute Selection, Rich Caruana and Dayne Freitag, Proceedings of the 11th International Conference on Machine Learning, 1994.

Discretization of Continuous Attributes

• Compression-Based Discretization of Continuous Attributes, B. Pfahringer, Proceedings of the 12th International Conference on Machine Learning, Morgan Kaufmann Publishers, San Francisco, CA. 1995.
• Global Discretization of Continuous Attributes as Preprocessing for Machine Learning, M.R. Chmielewski, J.W. Grzymala-Busse, International Journal of Approximate Reasoning, 15 (1996), 319-331.

Classification

• RainForst: A framework for fast decision tree construction of large datasets, In VLDB'98, pp. 416-427, New York, NY, 1998. (chosen by Zhihua Wen)
• Learning Trees and Rules with Set-valued Features, William W. Cohen, Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 1996.
• Boosting, Bagging, and C4.5, J. R. Quinlan, AAAI'96, pp 725-730
• Searching Classification Knowledge in Databases Based on Rough Sets. N. Shan, W. Ziarko, H. Hamilton, and N. Cercone, KDD-96, pp. 271-274, Portland, OR, August 1996

Association Rules

• Mining the Most Interesting Rules, Roberto J. Bayardo Jr., Rakesh Agrawal, In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, August 1999.
• H-Mine: Hyper-structure Mining of Frequent Patterns in Large Databases, J. Pei, J. Han, H. Lu, S. Tang, and D. YangProc. The 2001 IEEE International Conference on Data Mining (ICDM'01), San Jose, California, Novermber 29-December 2, 2001.(Chosen by Junjie Guo)
• Constraint-Based Rule Mining in Large, Dense Databases, Roberto J. Bayardo Jr., Rakesh Agrawal and Dimitrios Gunopulos, Proceedings of the 15th International Conference on Data Engineering, 1999 Mar, Sydney, Austrialia, IEEE CS Press, 1999
• Beyond Market Baskets: Generalizing Association Rules to Correlations, Craig Silverstein, Sergey Brin, Rajeev Motwani, Data Mining and Knowledge Discovery, 2, 1998, pp. 39-68
• Scalable Techniques for Mining Causal Structures, Craig Silverstein, Rajeev Motwani, Sergey Brin, and Jeff D. Ullman, Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), 1998

Clustering

• CACTUS-Clustering Categorical Data Using Summaries, Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan, Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), 1999 Aug, pp. 73-83
• Clustering Large Datasets in Arbitrary Metric Spaces, Venkatesh Ganti, Raghu Ramakrishnan, Johannes Gehrke, Allison L. Powell, James C. French, Proceedings of the 5th International Conference on Data Engineering, 23-26 March 1999, Sydney, Austrialia, IEEE CS Press, 1999, pp. 502-511
• ROCK: A Robust Clustering Algorithm for Categorical Attributes, Sudipto Guha, Rajeev Rastogi, Kyuseok Shim, Proceedings of the 15th International Conference on Data Engineering, 23-26 March 1999, Sydney, Austrialia, IEEE CS Press, 1999, pp. 512-521
• BIRCH: an efficient data clustering method for very large databases, Tian Zhang, Raghu Ramakrishnan, Miron Livny, Proceedings of the 1996 ACM SIGMOD international conference on Management of data , 1996, pp. 103-114
• CURE: An Efficient Clustering Algorithm for Large Databases, Sudipto Guha, Rajeev Rastogi, Kyuseok Shim, Proceedings of the ACM SIGMOD Conference, 1998
• A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, M. Ester M., H.-P. Kriegel, J. Sander, X. Xu, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, 1996, pp. 226-231
• Efficient and Effective Clustering Methods for Spatial Data Mining, Raymond T. Ng, Jiawei Han, Intelligent Database Systems Research Laboratory, Proc. of 1994 Int'l Conf. on Very Large Data Bases (VLDB'94), Santiago, Chile, September 1994, pp. 144-155

Web Mining

• Learning to Extract Symbolic Knowledge from the World Wide Web, M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam and S. Slattery, Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98), pp. 509-516, Madison, WI. AAAI Press. (Chosen by Linyan Wang)
• Mining the Link Structure of the World Wide Web, Soumen Chakrabarti, Byron E. Dom, S. Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins, David Gibson, and Jon Kleinberg, IEEE Computer, vol. 32, no. 8, August 1999. (Chosen by Jane Tao)
• Data mining of user navigation patterns, J. Borges and M. Levene, In Web Usage Analysis and User Profiling, pp. 92-111. Published by Springer-Verlag as Lecture Notes in Computer Science, Vol. 1836, 2000. (Chosen by Qian Wan)
• A Framework for Collaborative, Content-Based and Demographic Filtering, Michael J. Pazzani, Artificial Intelligence Review. (Chosen by Mariya Koshkina)

Spatial Mining

• Towards on-line analytical mining in large databases. J. Han, ACM SIGMOD Record, 27:97-107, 1998.
(Chosen by Jianliang Chen)
• Discovery of General Knowledge in Large Spatial Databases.W. Lu, J. Han and B. C. Ooi.FEGIS'93, pp. 275-289, Singapore, June 1993. (Chosen by Yu Li)

Sampling

• Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules, S.D. Lee, David W. Cheung, Ben Kao, Data Mining and Knowledge Discovery, An International Journal, Vol. 2, pp. 233-262, Kluwer Academic Publishers, 1998
• Sampling Large Databases for Association Rules, Hannu Toivonen, In 22th International Conference on Very Large Databases (VLDB'96), 134-145, Mumbay, India, September 1996. Morgan Kaufmann (Chosen by Bohdan Krushelnytskyy)

Visualization

• Visualization Techniques for Mining Large databases: A Comparison, Daniel Keim, Hans-Peter Kriegel, IEEE Trans. on Knowledge and Data Engineering, Vol. 8 No. 6, Dec. 1996
  

Miscellaneous

• A database perspective on knowledge discovery, Tomasz Imielinski and Heikki Mannila, Communications of the ACM, Vol. 39, No. 11 (Nov. 1996) Pages 58 - 64 (Chosen by Ying Zou)
• Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem, Mauricio A. Hernández, Salvatore J. Stolfo, Data Mining and Knowledge Discovery, Vol. 2, No. 1, 1998, pp. 9-37 (Chosen by Chenchen Xiao)
• Discovering Robust Knowledge from Databases that Change, Chun-Nan Hsu and Craig A. Knoblock, Data Mining and Knowledge Discovery, 2(1), 1998, pp. 69-95