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Finding interesting rules from large sets of discovered association rules
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Source Conference on Information and Knowledge Management archive
Proceedings of the third international conference on Information and knowledge management table of contents
Gaithersburg, Maryland, United States
Pages: 401 - 407  
Year of Publication: 1994
ISBN:0-89791-674-3
Authors
Mika Klemettinen  Department of Computer Science, University of Helsinki, P.O. Box 26, FIN-00014 University of Helsinki, Finland
Heikki Mannila  Department of Computer Science, University of Helsinki, P.O. Box 26, FIN-00014 University of Helsinki, Finland
Pirjo Ronkainen  Department of Computer Science, University of Helsinki, P.O. Box 26, FIN-00014 University of Helsinki, Finland
Hannu Toivonen  Department of Computer Science, University of Helsinki, P.O. Box 26, FIN-00014 University of Helsinki, Finland and Nokia Research Center
A. Inkeri Verkamo  Department of Computer Science, University of Helsinki, P.O. Box 26, FIN-00014 University of Helsinki, Finland
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
NIST : National Institue of Standards & Technology
UMBC : U of MD Baltimore County
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 13,   Downloads (12 Months): 148,   Citation Count: 123
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL


General Terms:
Documentation, Theory

Collaborative Colleagues:
Mika Klemettinen: colleagues
Heikki Mannila: colleagues
Pirjo Ronkainen: colleagues
Hannu Toivonen: colleagues
A. Inkeri Verkamo: colleagues

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