Incremental mining of frequent sequences from a window sliding over a stream of itemsets

Thomas Guyet 1, 2 René Quiniou 1
1 DREAM - Diagnosing, Recommending Actions and Modelling
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : We introduce the problem of mining frequent sequences in a window sliding over a stream of itemsets. To address this problem, we present a complete and correct incremental algorithm based on a representation of frequent sequences inspired by the PSP algorithm and a method for counting the minimal occurrences of a sequence. The experiments were conducted on simulated data and on real instantaneous power consumption data. The results show that our incremental algorithm significantly improves the computation time compared to a non-incremental approach.
Type de document :
Communication dans un congrès
Journées Intelligence Artificielle Fondamentale, May 2012, France. pp.153-162, 2012
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https://hal.archives-ouvertes.fr/hal-00757120
Contributeur : Thomas Guyet <>
Soumis le : lundi 26 novembre 2012 - 12:48:56
Dernière modification le : mercredi 2 août 2017 - 10:09:16

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  • HAL Id : hal-00757120, version 1

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Thomas Guyet, René Quiniou. Incremental mining of frequent sequences from a window sliding over a stream of itemsets. Journées Intelligence Artificielle Fondamentale, May 2012, France. pp.153-162, 2012. <hal-00757120>

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