CS831 Recommended Research Papers from 2001

• Y. Kachai and K. Waiyamai, Representing Large Concept Hierarchies Using Lattice Data Structure, PAKDD'2001, pp. 186-197 --- presented by Waqar Ahsan.
• S. Sarawagi, iDiff: Informative Summarization of Differences in Multidimensional Aggregates, Data Mining and Knowledge Discovery, 5(4):255-276, 2001.

• J. Li, K. Ramamohanarao, G. Dong, Combining the Strength of Pattern Frequency and Distance for Classification, PAKDD'2001, pp. 455-466 -- presented by Mahesh Shrestha.

• T. Scheffer, Finding Association Rules that Trade Support Optimally Against Confidence, PKDD 2001.
• J. Han, J. Pei, G. Dong, and K. Wang, `` 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.
• M. Seno, and G. Karypis, LPMiner: An Algorithm for Finding Frequent Itemsets Using Length-Decreasing Support Constraint, ICDM 2001 -- Yashu Bither.

• Z. Bi, C. Faloutsos, F. Korn, The ``DGX'' Distribution for Mining Massive, Skewed Data, KDD 2001.
• T. Morzy, M. Wojciechowski, and M. Zakrzewicz, Scalable Hierarchical Clustering Method for Sequences of Categorical Values, PAKDD 2001, pp. 282-293.
• N. Pernelle, M.-C. Rousset and V. Ventos, Automatic Construction and Refinement of a Class Hierarchy over Multi-valued Data, PKDD 2001, pp. 386-398 --- presented by Yan Zhao.
• Kalton, A., Wagstaff, K., and Yoo, J., "Generalized Clustering, Supervised Learning, and Data Assignment," KDD'2001.

• D. Lambert and J. Pinheiro, Mining a Stream of Transactions for Customer Patterns, Proceedings of the ACM SIGKDD 2001. http://cm.bell-labs.com/cm/ms/departments/sia/dl/pub.html
• G. Hulten, L. Spencer, and P. Domingos, Mining Time-Changing Data Streams, Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, 2001. San Francisco, CA: ACM Press. www.cs.washington.edu/homes/ghulten/papers/cvfdt-kdd01.pdf

• J. Yang, W. Wang, and P. Yu, InfoMiner: Mining Surprising Periodic Patterns, Proceedings of the ACM SIGKDD 2001. http://www.cs.ucla.edu/~weiwang/paper/KDD01.ps
• S. Jaroszewicz and D. Simovici, A General Measure of Rule Interestingness, PKDD'2001.
• Sigal Sahar, Interestingness PreProcessing, ICDM 2001 --- presented by Xing Li.

• H. Mannila and M. Salmenkivi, Finding simple intensity descriptions from event sequence data, Proceedings of the Seventh ACM SIGKDD International Conference, on Knowledge Discovery and Data Mining (KDD 2001), pp. 341-346.
• F. Hoeppner, Discovery of Temporal Patterns: Learning Rules about the Qualitative Behaviour of Time Series, Principles of Data Mining and Knowledge Discovery (PKDD'2001), 2001. --- presented by Guichong Li
• M. Zhang, B. Kao, C.-L. Yip, and D. Cheung, FFS -- An I/O Effieicient Algorithm for Mining Frequent Sequences, PAKDD'2001, pp. 294-305.
• J. X. Yu, M. K. Ng, and J. Z. Huang, Patterns Discovery Based on Time-Series Decomposition, PAKDD'2001, pp. 336-347.
• S. Chu, E. Keogh, D. Hart, M. Pazzani, Iterative Deepening Dynamic Time Warping for Time Series, SIAM KDD 2002.
• E. Keogh, S. Chu, D. Hart, M. Pazzani, An Online Algorithm for Segmenting Time Series, ICDM 2001.

• C.-H. Chang, S.-C. Lui, and Y.-C. Wu, Applying Pattern Mining to Web Information Extraction, PAKDD'2001, pp. 4-15.
• T.W. Hong and K.L. Clark, Using Grammatical Inference to Automate Information Extraction from the Web, PKDD 2001.
• B. Liu, Y. Ma, and P. Yu, Discovering Unexpected Information from Your Competitors' Web Sites, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001) --- presented by Zhiyong Lu.
• B. Padmanabhan, Z. Zheng, and S. O. Kimbrough, Personalization from Incomplete Data: What You Don't Know Can Hurt, KDD 2001 -- presented by Yawen Wu.
• Krishna Bharat, Bay-Wei Chang, Monika Henzinger, Matthias Ruhl, Who Links to Whom: Mining Linkage between Web Sites, Proceedings IEEE International Conference on Data Mining (ICDM), San Jose, November 2001.

• Wei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, and P. Chan, Using Artificial Anomalies to Detect Unknown and Known Network Intrusions, ICDM 2001.
• L. Portnoy, E. Eskin, and S. Stolfo, Intusion Dectection with Unlabeled Data Using Clustering -- presented by Thamar Mora. unknown

• D. M. Pennock, S. Lawrence, C. Lee Giles, and F. A. Nielsen, Extracting collective probabilistic forecasts from Web games, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001).
• Y. Yang and G. I. Webb, Proportional k-interval Discretization for Naive-Bayes Classifiers, PKDD'2001 -- presented by Yao Hong.

• Tri-Plots: Scalable Tools for Multidimensional Data Mining , Traina, A., Faloutsos, C., Papadimitriou, S., Traina, C., The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'01), San Francisco, CA, August 19-26, 2001. http://www.informedia.cs.cmu.edu/documents/traina-kdd01-triplots.pdf
• Noel, S., Raghavan, V., and C.-H. H. Chu, Visualizing Association Mining Results through Hierarchical Clusters, ICDM'2001 --- presented by Yuancheng Liu.

• S. Mani and G. F. Cooper, ``A Simulation Study of Three Related Causal Data Mining Algorithms'', Proceedings of the International Workshop on Artificial Intelligence and Statistics, 2001, p73--80. Morgan Kaufmann, San Francisco, CA. http://www.cbmi.upmc.edu/~mani/pub/publications.html">