Levelwise search of frequent patterns with counting inference

Abstract : In this paper,we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database, and we propose a new method called pattern counting inference, that allows to perform as few support counts as possible. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the Pascal algorithm that is an optimization of the simple and efficient Apriori Algorithm. Experiments comparing Pascal to the Apriori, Close and Max-Miner algorithms, each one representative of a frequent patterns discovery strategy, show that Pascal improves the efficiency of the frequent pattern extraction from correlated data and that it does not induce additional execution times when data is weakly correlated.
Type de document :
Communication dans un congrès
Bases de Données Avancées - BDA'00, Oct 2000, Blois, 16 p, 2000
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Soumis le : mardi 26 septembre 2006 - 08:49:43
Dernière modification le : jeudi 18 janvier 2018 - 02:10:41
Document(s) archivé(s) le : mercredi 29 mars 2017 - 12:40:06



  • HAL Id : inria-00099065, version 1


Yves Bastide, Rafik Taouil, Nicolas Pasquier, Gerd Stumme, Lotfi Lakhal. Levelwise search of frequent patterns with counting inference. Bases de Données Avancées - BDA'00, Oct 2000, Blois, 16 p, 2000. 〈inria-00099065〉



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