Association Rules

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. Expanded version available as IBM Research Report RJ9839, June 1994.
Hannu Toivonen: "Sampling large databases for association rules" In 22th International Conference on Very Large Databases (VLDB'96),134 - 145, Mumbay, India, September 1996. Morgan Kaufmann.
R. Srikant, Q. Vu, R. Agrawal: "Mining Association Rules with Item Constraints", Proc. of the 3rd Int'l Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, August 1997.
Mannila, Toivonen:"Multiple uses of frequent sets and condensed representations",KDD 1996.
R. Srikant, R. Agrawal: "Mining Quantitative Association Rules in Large Relational Tables", Proc. of the ACM-SIGMOD 1996 Conference on Management of Data, Montreal, Canada, June 1996.
Yonatan Aumann, Yehuda Lindell: "A Statistical Theory for Quantitative Association Rules". 261-270, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, 1999, San Diego, CA, USA. ACM, 1999
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan: "Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications", Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Seattle, Washington, June 1998.
Luc Dehaspe and Hannu Toivonen:"Frequent query discovery: a unifying ILP approach to association rule mining", Report CW-258, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, March 1998.

Decision Trees

Wei-Yin Loh, Y.-S. Shih: Split selection methods for classification trees. Statistica Sinica, 1997, vol. 7, pp. 815-840.
Lim, T.-S., Loh, W.-Y., and Shih, Y.-S. (2000), A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms, Machine Learning Journal, vol. 40, pp. 203-228.
QUEST Hompage
Haixun Wang, Carlo Zaniolo: CMP: A Fast Decision Tree Classifier Using Multivariate Predictions,. ICDE 2000: 449-460
Ankerst M., Elsen C., Ester M., Kriegel H.-P.: Visual Classification: An Interactive Approach to Decision Tree Construction, Proc. 5th Int. Conf. on Knowledge Discovery and Data Mining (KDD'99), San Diego, CA, 1999, pp. 392-396. PS-Version
Ankerst M., Ester M., Kriegel H.-P.: Towards an Effective Cooperation of the Computer and the User for Classification, Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining (KDD'2000), Boston, MA, 2000.

Clustering

Ankerst M., Breunig M. M., Kriegel H.-P., Sander J.: OPTICS: Ordering Points To Identify the Clustering Structure, Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'99), Philadelphia, PA, 1999, pp. 49-60.
Ester M., Kriegel H.-P., Sander J., Xu X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proc. 2nd Int. Conf.on Knowledge Discovery and Data Mining (KDD'96), Portland, OR, 1996, pp. 226-231.
Breunig S., Kriegel H.-P., Ng R., Sander J.: LOF: Identifying Density-Based Local Outliers, Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2000), Dallas, TX, 2000. Clustering Home-Page München
Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim: Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD Conference 2000: 427-438
David Gibson, Jon M. Kleinberg, Prabhakar Raghavan: Clustering Categorical Data: An Approach Based on Dynamical Systems. VLDB 1998: 311-322
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim: ROCK: A Robust Clustering Algorithm for Categorical Attributes. ICDE 1999: 512-521
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan: CACTUS - Clustering Categorical Data Using Summaries. KDD 1999: 73-83
Charu C. Aggarwal, Cecilia Magdalena Procopiuc, Joel L. Wolf, Philip S. Yu, Jong Soo Park: Fast Algorithms for Projected Clustering. SIGMOD Conference 1999: 61-72
Charu C. Aggarwal, Philip S. Yu: Finding Generalized Projected Clusters In High Dimensional Spaces. SIGMOD Conference 2000: 70-81