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Mining concise representations of frequent patterns through conjunctive and disjunctive search spaces

Mining concise representations of frequent patterns through conjunctive and disjunctive search... Mining Concise Representations of Frequent Patterns through Conjunctive and Disjunctive Search Spaces Tarek Hamrouni Computer Science Department, Faculty of Sciences of Tunis, Tunis El Manar University, Tunisia tarek.hamrouni@fst.rnu.tn Computer Science Research Center of Lens, Artois University, France hamrouni@cril.univ-artois.fr The last years witnessed an explosive progress in networking, storage, and processing technologies resulting in an unprecedented amount of digitalization of data. There is hence a considerable need for tools or techniques to delve and e ƒciently discover valuable, non-obvious information from large databases. In this situation, Knowledge Discovery in Databases o €ers a complete process for the non-trivial extraction of implicit, previously unknown, and potentially useful knowledge from data. Amongst its steps, data mining o €ers tools and techniques for such an extraction. Much research in data mining from large databases has focused on the discovery of association rules which are used to identify relationships between sets of items in a database. The discovered association rules can be used in various tasks, such as depicting purchase dependencies, classi cation, medical data analysis, etc. In practice however, the number of frequently occurring itemsets, used as a basis for rule derivation, is very large, hampering their e €ective exploitation by the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGKDD Explorations Newsletter Association for Computing Machinery

Mining concise representations of frequent patterns through conjunctive and disjunctive search spaces

Hamrouni, Tarek
ACM SIGKDD Explorations Newsletter , Volume 11 (1) – Nov 16, 2009
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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Pattern analysis
ISSN
1931-0145
DOI
10.1145/1656274.1656288
Publisher site
See Article on Publisher Site

Abstract

Mining Concise Representations of Frequent Patterns through Conjunctive and Disjunctive Search Spaces Tarek Hamrouni Computer Science Department, Faculty of Sciences of Tunis, Tunis El Manar University, Tunisia tarek.hamrouni@fst.rnu.tn Computer Science Research Center of Lens, Artois University, France hamrouni@cril.univ-artois.fr The last years witnessed an explosive progress in networking, storage, and processing technologies resulting in an unprecedented amount of digitalization of data. There is hence a considerable need for tools or techniques to delve and e ƒciently discover valuable, non-obvious information from large databases. In this situation, Knowledge Discovery in Databases o €ers a complete process for the non-trivial extraction of implicit, previously unknown, and potentially useful knowledge from data. Amongst its steps, data mining o €ers tools and techniques for such an extraction. Much research in data mining from large databases has focused on the discovery of association rules which are used to identify relationships between sets of items in a database. The discovered association rules can be used in various tasks, such as depicting purchase dependencies, classi cation, medical data analysis, etc. In practice however, the number of frequently occurring itemsets, used as a basis for rule derivation, is very large, hampering their e €ective exploitation by the

Journal

ACM SIGKDD Explorations Newsletter – Association for Computing Machinery

Published: Nov 16, 2009

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