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Finding Interesting Rules from Large Sets of Discovered Association Rules.

Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo: Finding Interesting Rules from Large Sets of Discovered Association Rules. CIKM 1994: 401-407
@inproceedings{DBLP:conf/cikm/KlemettinenMRTV94,
  author    = {Mika Klemettinen and
               Heikki Mannila and
               Pirjo Ronkainen and
               Hannu Toivonen and
               A. Inkeri Verkamo},
  title     = {Finding Interesting Rules from Large Sets of Discovered Association
               Rules},
  booktitle = {Proceedings of the Third International Conference on Information
               and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November
               29 - December 2, 1994},
  publisher = {ACM},
  year      = {1994},
  pages     = {401-407},
  ee        = {db/conf/cikm/KlemettinenMRTV94.html, http://doi.acm.org/10.1145/191246.191314},
  crossref  = {DBLP:conf/cikm/94},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form "for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B". Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.

Copyright © 1994 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November 29 - December 2, 1994. ACM 1994
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Referenced by

  1. Themistoklis Palpanas: Knowledge Discovery in Data Warehouses. SIGMOD Record 29(3): 88-100(2000)
  2. Theodore Johnson, Laks V. S. Lakshmanan, Raymond T. Ng: The 3W Model and Algebra for Unified Data Mining. VLDB 2000: 21-32
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  15. Sergey Brin, Rajeev Motwani, Craig Silverstein: Beyond Market Baskets: Generalizing Association Rules to Correlations. SIGMOD Conference 1997: 265-276
  16. Heikki Mannila: Methods and Problems in Data Mining. ICDT 1997: 41-55
  17. Brian Lent, Arun N. Swami, Jennifer Widom: Clustering Association Rules. ICDE 1997: 220-231
  18. David Wai-Lok Cheung, Sau Dan Lee, Ben Kao: A General Incremental Technique for Maintaining Discovered Association Rules. DASFAA 1997: 185-194
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  22. Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)
  23. Hannu Toivonen: Sampling Large Databases for Association Rules. VLDB 1996: 134-145
  24. Kimmo Hätönen, Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen: Knowledge Discovery from Telecommunication Network Alarm Databases. ICDE 1996: 115-122
  25. David Wai-Lok Cheung, Jiawei Han, Vincent T. Y. Ng, C. Y. Wong: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. ICDE 1996: 106-114
  26. Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431
  27. Jiawei Han: Mining Knowledge at Multiple Concept Levels. CIKM 1995: 19-24
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