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A condensed representation to find frequent patterns
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Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems table of contents
Santa Barbara, California, United States
Pages: 267 - 273  
Year of Publication: 2001
ISBN:1-58113-361-8
Authors
Artur Bykowski  Laboratoire d'Ingénierie des Systèmes d'Information, INSA Lyon, Bâtiment 501, F-69621 Villeurbanne Cedex, France
Christophe Rigotti  Laboratoire d'Ingénierie des Systèmes d'Information, INSA Lyon, Bâtiment 501, F-69621 Villeurbanne Cedex, France
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 21,   Citation Count: 18
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ABSTRACT

Given a large set of data, a common data mining problem is to extract the frequent patterns occurring in this set. The idea presented in this paper is to extract a condensed representation of the frequent patterns called disjunction-free sets, instead of extracting the whole frequent pattern collection. We show that this condensed representation can be used to regenerate all frequent patterns and their exact frequencies. Moreover, this regeneration can be performed without any access to the original data. Practical experiments show that this representation can be extracted very efficiently even in difficult cases. We compared it with another representation of frequent patterns previously investigated in the literature called frequent closed sets. In nearly all experiments we have run, the disjunction-free sets have been extracted much more efficiently than frequent closed sets.


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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H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In Proceedings KDD'96, pages 189-194, Portland, USA, Aug. 1996.
 
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J. Pei, J. Han, and R. Mao. Closet: An efficient algorithm for mining frequent closed itemsets. In Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 21-30, Dallas, Texas, May 2000.
 
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CITED BY  18

Collaborative Colleagues:
Artur Bykowski: colleagues
Christophe Rigotti: colleagues