Secondary List of Papers

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1. Introduction and Overview

Data Mining and Database Systems: Where is the Intersection, Surajit Chaudhuri, IEEE bulletin of technical committee on Data Engineering, March 1998, Vol. 21 No. 1

Comprehensible Knowledge Discovery: Gaining Insight from Data, Pazzani. M, First Federal Data Mining Conference and Exposition. pg 73-82. Washington, DC.

2. Data Warehousing and OLAP

OLAP Mining: An Integration of OLAP with Data Mining, J. Han, Proc. 1997 IFIP Conference on Data Semantics (DS-7), Leysin, Switzerland, Oct. 1997, pp. 1-11.

3. Data Reduction, Feature Selection/Extraction and Clustering

Clustering Data Without Distance Functions, G.D. Ramkumar and Arun Swami, IEEE bulletin of technical committee on Data Engineering, March 1998, Vol. 21 No. 1

Hypergraph Based Clustering in High-Diminsional Data Sets: A Summary of Results Eui-Hong Han, George Karypis, Vipin Kumar, and Bamshar, IEEE bulletin of technical committee on Data Engineering, March 1998, Vol. 21 No. 1

4. Association Rules

Fast Algorithms for Mining Association Rules, R. Agrawal, R. Srikant, Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, Sept. 1994. Expanded Version version available IBM Research Report RJ9839.
Mining Generalized Association Rules, R. Srikant, R. Agrawal,Proc. of the 21st Int'l Conference on Very Large Databases, Zurich, Switzerland, Sep. 1995. Expanded version available as IBM Research Report RJ 9963, June 1995.

Mining Association Rules with Item Constraints, R. Srikant, Q. Vu, R. Agrawal, Proc. of the 3rd Int'l Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, August 1997.

Learning hierarchical rule sets, J. Kivinen, H. Mannila, and E. Ukkonen, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, July 27-29, 1992, p. 37-44.

Pruning and grouping discovered association rules. H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hätönen, and H. Mannila, In MLnet Workshop on Statistics, Machine Learning and Discovery in Databases, 47-52, Heraklion, Crete, Greece, April 1995.

Finding interesting rules from large sets of discovered association rules.M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and I. Verkamo, In Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), 401-407, Gaithersburg, Maryland, November 1994. ACM Press.

Levelwise search and borders of theories in knowledge discovery, H. Mannila and Hannu Toivonen, University of Helsinki, Department of Computer Science, January 1997.

Data mining as selective theory extraction in probabilistic logic. Manfred Jaeger and H. Mannila and Emil Weydert, In SIGMOD '96 Workshop on Knowledge Discovery.

5. Graphical Models for Discovering Knowledge

Generalization and Decision Tree Induction: Efficient Classification in Data Mining, M. Kamber, L. Winstone, W. Gong, S. Cheng, and J. Han, Proc. of 1997 Int'l Workshop on Research Issues on Data Engineering (RIDE'97), Birmingham, England, April 1997, pp. 111-120.

Embedded Bayesian Network Classifiers, D. Heckerman and C. Meek, Technical Report MSR-TR-97-06, Microsoft Research, March, 1997.

6. Classification

SLIQ: A Fast Scalable Classifier for Data Mining, M. Mehta, R. Agrawal and J. Rissanen, Proc. of the Fifth Int'l Conference on Extending Database Technology, Avignon, France, March 1996.

Models and Selection Criteria for Regression and Classification, D. Heckerman and C. Meek. Technical Report MSR-TR-97-08, Microsoft Research, May, 1997.

An Interval Classifier for Database Mining Applications, R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, A. Swami: ", Proc. of the 18th Int'l Conference on Very Large Databases, Vancouver, Aug. 1992, pp 560-573.

7. Mining of Sequential Patterns and Time Series

Similarity of Event Sequences (revised version), Heikki Mannila and Pirjo Ronkainen: . In Proceedings of the Fourth International Workshop on Temporal Representation and Reasoning (TIME'97) , 10th - 11th May, 1997, Daytona Beach, Florida, USA, p. 136-139.

Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases R. Agrawal, K. Lin, H. S. Sawhney, K. Shim, Proc. of the 21st Int'l Conference on Very Large Databases, Zurich, Switzerland, September 1995.

Mining Temporal Relationships with Multiple Granularities in Time Sequences, Claudio Bettini, X. Sean Wang, and Sushil Jajodia, IEEE bulletin of technical committee on Data Engineering, March 1998, Vol. 21 No. 1

Discovering generalized episodes using minimal occurrences.Heikki Mannila and Hannu Toivonen: In Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, August 2-4, 1996.

8. Computational Aspects

The power of sampling in knowledge discovery Jyrki Kivinen and Heikki Mannila, In Proceedings of the 1994 ACM SIGACT-SIGMOD-SIGACT Symposium on Principles of Database Theory (PODS'94) , Minneapolis, MN, May 1994, p. 77-85.

Active Data Mining, R. Agrawal, G. Psaila, Proc. of the 1st Int'l Conference on Knowledge Discovery and Data Mining, Montreal, August 1995.

9. Visualization/Integration

Visualization Techniques for Mining Large Databases: A Comparison, Keim D. A., Kriegel H.-P., Proc.Transactions on Knowledge and Data Engineering, Special Issue on Data Mining, Vol. 8, No. 6, 1996, pp. 923-938.

Visualizing the Simple Bayesian Classifier, Becker B., Kohavi, R., Sommerfield D., Appears in
the KDD 1997 Workshop on Issues in the Integration of Data Mining and Data Visualization. To
appear as Lecture Notes in Computer Science by Springer Verlag.

A Metapattern-Based Automated Discovery Loop for Integrated Data Mining, IEEE tansactions on Data and Knowledge Engineering, 8(6)898-910. 1996.

10. Miscellaneous

What Makes Patterns Interesting in Knowledge Discovery Systems, Silberschatz, A. and A. Tuzhilin, IEEE Transactions on Knowledge and Data Engineering 8, 6 (December 1996), 970-974.

Distance Measures for Point Sets and Their Computation , Thomas Eiter and Heikki Mannila: Acta Informatica, 34, 2, 109-133, 1997.

Dealing with Semantic Heterogeneity by Generalization-Based Data Mining Techniques, J. J. Han, R. T. Ng, Y. Fu, and S. Dao, M. P. Papazoglou and G. Schlageter (eds.), Cooperative Information Systems: current Trends & Directions, Academic Press, 1998, pp. 207-231.

Mining for Complex Patterns in Large Databases Using Unexpectedness, C. Jermaine and R. J. Miller. Submitted for publication.