Article

In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules

Author:

Einoshin SuzukiAuthors Info & Claims

Progress in Discovery Science, Final Report of the Japanese Discovery Science Project

Pages 504 - 517

Published: 01 January 2002 Publication History

Abstract

This paper reports our progress on interesting pattern discovery in the discovery science project. We first introduce undirected discovery of exception rules, in which a pattern represents a pair of an exception rule and its corresponding strong rule. Then, we explain scheduled discovery, exception rule discovery guided by a meta-pattern, and data mining contests as our contribution to the project. These can be classified as pattern search, pattern representation, and scheme justification from the viewpoint of research topics in interesting pattern discovery.

References

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B. R. Gaines: "Transforming Rules and Trees", Advances in Knowledge Discovery and Data Mining , AAAI/MIT Press, Menlo Park, Calif., pp. 205-226, 1996.

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F. Hussain, H. Liu, E. Suzuki, and H. Lu: "Exception Rule Mining with a Relative Interestingness Measure", Knowledge Discovery and Data Mining (PAKDD) , LNAI 1805, Springer, pp. 86-97, 2000.

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R. Kohavi, C. E. Brodley, B. Frasca, L. Mason, and Z. Zheng: "KDD-Cup 2000 Organizers Report: Peeling the Onion", ACM SIGKDD Explorations , Vol. 2, No. 1, pp. 86-93, 2001.

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B. Liu, W. Hsu, L. Mun, and H. Lee: "Finding Interesting Patterns Using User Expectations", IEEE Trans. Knowledge and Data Eng. , Vol. 11, No. 6, pp. 817-832, 1999.

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B. Liu, W. Hsu, and Y. Ma: "Pruning and Summarizing the Discovered Associations", Proc. Fifth ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD) , pp. 125-134, 1999.

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C. J. Merz and P. M. Murphy: "UCI Repository of Machine Learning Databases", http://www.ics.uci.edu/~mlearn/MLRepository.html Dept. of Information and Computer Sci., Univ. of California Irvine, 1996.

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S. Sugaya, E. Suzuki, and S. Tsumoto: "Support Vector Machines for Knowledge Discovery", Principles of Data Mining and Knowledge Discovery (PKDD) , LNAI 1704, Springer, pp. 561-567, 1999.

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S. Sugaya, E. Suzuki, and S. Tsumoto: "Instance Selection Based on Support Vector Machine for Knowledge Discovery in Medical Database", Instance Selection and Construction for Data Mining , pp. 395-412, Kluwer, Norwell, Mass., 2001.

[15]

E. Suzuki and M. Shimura: "Exceptional Knowledge Discovery in Databases Based on Information Theory", Proc. Second Int'l Conf. on Knowledge Discovery and Data Mining (KDD) , pp. 275-278, 1996.

[16]

E. Suzuki: "Autonomous Discovery of Reliable Exception Rules", Proc. Third Int'l Conf. on Knowledge Discovery and Data Mining (KDD) , pp. 259-262, 1997.

[17]

E. Suzuki and Y. Kodratoff: "Discovery of Surprising Exception Rules Based on Intensity of Implication", Principles of Data Mining and Knowledge Discovery (PKDD) , LNAI 1510, Springer, pp. 10-18, 1998.

[18]

E. Suzuki: "Scheduled Discovery of Exception Rules", Discovery Science (DS) , LNAI 1721, Springer, pp. 184-195, 1999.

[19]

E. Suzuki and S. Tsumoto: "Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets", Knowledge Discovery and Data Mining (PAKDD) , LNAI 1805, Springer, pp. 208-211, 2000.

[20]

E. Suzuki: "Mining Bacterial Test Data with Scheduled Discovery of Exception Rules", Proc. Int'l Workshop of KDD Challenge on Real-World Data (KDD Challenge) , pp. 34-40, 2000.

[21]

E. Suzuki (ed.): Proc. Int'l Workshop of KDD Challenge on Real-World Data (KDD Challenge) , (http://www.slab.dnj.ynu.ac.jp/challenge2000), 2000.

[22]

E. Suzuki and J. M. Zytkow: "Unified Algorithm for Undirected Discovery of Exception Rules", Principles of Data Mining and Knowledge Discovery (PKDD) , LNAI 1910, Springer, pp. 169-180, 2000.

[23]

E. Suzuki: "Issues in Organizing a Successful Knowledge Discovery Contest", Discovery Science (DS) , LNAI 1967, Springer, pp. 282-284, 2000.

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S. Tsumoto et al.: "Comparison of Data Mining Methods using Common Medical Datasets", ISM Symposium: Data Mining and Knowledge Discovery in Data Science , pp. 63-72, 1999.

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N. Yugami, Y. Ohta, and S. Okamoto: "Fast Discovery of Interesting Rules", Knowledge Discovery and Data Mining (PAKDD) , LNAI 1805, Springer, pp. 17-28, 2000.

Cited By

Fisch DGruhl CKalkowski ESick BOvaska S(2016)Towards automation of knowledge understandingInformation Sciences: an International Journal10.1016/j.ins.2016.08.016370:C(476-496)Online publication date: 20-Nov-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.08.016
Plantevit MGoutier SGuisnel FLaurent ATeisseire MSong IPedersen T(2007)Mining unexpected multidimensional rulesProceedings of the ACM tenth international workshop on Data warehousing and OLAP10.1145/1317331.1317347(89-96)Online publication date: 9-Nov-2007
https://dl.acm.org/doi/10.1145/1317331.1317347

Index Terms

In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

Progress in Discovery Science, Final Report of the Japanese Discovery Science Project

January 2002

681 pages

ISBN:3540433384

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2002

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Cited By

Fisch DGruhl CKalkowski ESick BOvaska S(2016)Towards automation of knowledge understandingInformation Sciences: an International Journal10.1016/j.ins.2016.08.016370:C(476-496)Online publication date: 20-Nov-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.08.016
Plantevit MGoutier SGuisnel FLaurent ATeisseire MSong IPedersen T(2007)Mining unexpected multidimensional rulesProceedings of the ACM tenth international workshop on Data warehousing and OLAP10.1145/1317331.1317347(89-96)Online publication date: 9-Nov-2007
https://dl.acm.org/doi/10.1145/1317331.1317347

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