Summarizing itemset patterns: A profile-based approach

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2005, Pages 314-323

  Yan, Xifeng ^a   Cheng, Hong ^a   Han, Jiawei ^a   Xin, Dong ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Frequent pattern; Probabilistic model; Summarization

Indexed keywords

FREQUENT PATTERN; PROBABILISTIC MODEL; SUMMERIZATION;

DATA ACQUISITION; GRAPH THEORY; HEURISTIC METHODS; OPTIMIZATION; PROBABILISTIC LOGICS;

DATA MINING;

EID: 32344439809     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1081870.1081907     Document Type: Conference Paper

References (27)

1. F. Afrati, A. Gionis, and H. Mannila. Approximating a collection of frequent sets. In Proc. of 2004 ACM Int. Conf. on Knowledge Discovery in Databases (KDD'04), pages 12 - 19, 2004.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of 1993 Int. Conf., on Management of Data (SIGMOD'93), pages 207-216, 1993.
3. R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. of 1995 Int. Conf. on Data Engineering (ICDE'95), pages 3-14, 1995.
4. L. Baker and A. McCallum. Distributional clustering of words for text classification. In Proc. of 1998 ACM Int. Conf. on Research and Development in Information Retrieval (SIGIR'98), pages 96-103, 1998.
5. R. Bayardo. Efficiently mining long patterns from databases. In Proc. of 1998 Int. Conf., on Management of Data (SIGMOD'98), pages 85-93, 1998.
6. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proc. of 2002 European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD'02), pages 74-85, 2002.
7. L. Dehaspe, H. Toivonen, and R. King. Finding frequent substructures in chemical compounds. In Proc. of 1998 Int. Conf. on Knowledge Discovery and Data Mining (KDD'98), pages 30-36, 1998.
8. M. Deshpande, M. Kuramochi, and G. Karypis. Frequent sub-structure-based approaches for classifying chemical compounds. In Proc. of 2003 Int. Conf. on Data Mining (ICDM'03), pages 35-42, 2003.
9. I. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classification. J. of Machine Learning Research, 3:1265-1287, 2003.
10. D. Gunopulos, H. Mannila, R. Khardon, and H. Toivonen. Data mining, hypergraph transversals, and machine learning. In Proc. 1997 ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS'97), pages 209 - 216, 1997.
11. J. Han, J. Wang, Y. Lu, and P. Tzvetkov. Mining top-k frequent closed patterns without minimum support. In Proc. of 2002 Int. Conf. on Data Mining (ICDM'02), pages 211-218, 2002.
12. W. Hoeffding. Probability inequalities for sums of bounded random variables. J. American Statistical Associations, 58:13-30, 1963.
13. L. Holder, D. Cook, and S. Djoko. Substructure discovery in the subdue system. In Proc. AAAI94 Workshop on Knowledge Discovery in Databases (KDD94), page 169180, 1994.
14. J. Huan, W. Wang, D. Bandyopadhyay, J. Snoeyink, J. Prins, and A. Tropsha. Mining spatial motifs from protein structure graphs. In Proc. of 8th Ann. Int. Conf. on Research in Computational Molecular Biology (RECOMB'04), pages 308-315, 2004.
15. M. Hutchins, H. Foster, T. Goradia, and T. Ostrand. Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria. In Proc. of 16th Int. Conf. on Software engineering (ICSE'94), pages 191-200, 1994.
16. R. Kohavi, C. Brodley, B. Frasca, L, Mason, and Z. Zheng. KDD-Cup 2000 organizers' report: Peeling the onion. SIGKDD Explorations, 2:86-98, 2000.
17. T. Mielikäinen and H. Mannila. The pattern ordering problem. In Prof. 7th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'03), pages 327-338, 2003.
18. E. Omiecinski. Alternative interest measures for mining associations. IEEE Trans. Knowledge and Data Engineering, 15:57-69, 2003.
19. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. of 7th Int. Conf. on Database Theory (ICDT'99), pages 398-416, 1999.
20. D. Pavlov, H. Mannila, and P. Smyth. Beyond independence: Probabilistic models for query approximation on binary transaction data. IEEE Trans. Knowledge and Data Engineering, 15:1409-1421, 2003.
21. J. Pei, G. Dong, W. Zou, and J. Han. On computing condensed frequent pattern bases. In Proc. 2002 Int. Conf. on Data Mining (ICDM'02), pages 378-385, 2002.
22. J. Pei, A. Tung, and J. Han. Fault-tolerant frequent pattern mining: Problems and challenges. In Proc. of 2001 ACM Int. Workshop Data Mining and Knowledge Discovery (DMKD'01), pages 7-12, 2001.
23. M. Steinbach, P. Tan, and V. Kumar. Support envelopes: a technique for exploring the structure of association patterns. In Proc. of 2002 ACM Int. Conf. on Knowledge Discovery in Databases (KDD'04), pages 296 - 305, 2004.
24. P. Tan, V. Kumar, and J. Srivastava. Selecting the right interestingness measure for association patterns. In Proc. of 2002 ACM Int. Conf. on Knowledge Discovery in Databases (KDD'02), pages 32-41, 2002.
25. K. Wang, C. Xu, and B. Liu. Clustering transactions using large items. In Proc. of 8th Int. Conf. on Information and Knowledge Management (CIKM'99), pages 483 - 490, 1999.
26. X. Yan, P. Yu, and J. Han. Graph indexing: A frequent structure-based approach. In Proc. of 2004 ACM Int. Conf. on Management of Data (SIGMOD'04), pages 335-346, 2004.
27. C. Yang, U. Fayyad, and P. S. Bradley. Efficient discovery of error-tolerant frequent itemsets in high dimensions. In Proc. of 2001 ACM Int. Conf. on Knowledge Discovery in Databases (KDD'01), pages 194-203, 2001.