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A model for managing collections of patterns
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Proceedings of the 2007 ACM symposium on Applied computing table of contents
Seoul, Korea
SESSION: Information access and retrieval table of contents
Pages: 860 - 865  
Year of Publication: 2007
ISBN:1-59593-480-4
Authors
Baptiste Jeudy  Univ. of St-Etienne, France
Christine Largeron  Univ. of St-Etienne, France
François Jacquenet  Univ. of St-Etienne, France
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns that could impact their business. Nevertheless, those users are often overwhelmed by the large quantity of patterns extracted in such a situation. Moreover, some privacy issues, or some commercial one may lead the users not to be able to mine the data by themselves. Thus, the users may not have the possibility to perform many experiments integrating various constraints in order to focus on specific patterns they would like to extract. Post processing of patterns may be an answer to that drawback. Thus, in this paper we present a framework that could allow end users to manage collections of patterns. We propose to use an efficient data structure on which some algebraic operators may be used in order to retrieve or access patterns in pattern bases.


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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Collaborative Colleagues:
Baptiste Jeudy: colleagues
Christine Largeron: colleagues
François Jacquenet: colleagues