Vers une mesure de similarité pour les séquences complexes
Identifieur interne : 001323 ( Main/Exploration ); précédent : 001322; suivant : 001324Vers une mesure de similarité pour les séquences complexes
Auteurs : Elias Egho [France] ; Chedy Raïssi [France] ; Toon Calders [Belgique] ; Nicolas Jay [France] ; Amedeo Napoli [France]Source :
- Revue des Nouvelles Technologies de l'Information [ 1764-1667 ] ; 2013-02-02.
Abstract
Computing the similarity between sequences is a very important challenge for many different data mining tasks. There is a plethora of similarity measures for sequences in the literature, most of them being designed for sequences of items. In this work, we study the problem of measuring the similarity ratio between sequences of itemsets. We present new combinatorial results for efficiently counting distinct and common subsequences. These theoretical results are the cornerstone for an effective dynamic programming approach to deal with this problem.
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Le document en format XML
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<series><title level="j">Revue des Nouvelles Technologies de l'Information</title>
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<front><div type="abstract" xml:lang="en">Computing the similarity between sequences is a very important challenge for many different data mining tasks. There is a plethora of similarity measures for sequences in the literature, most of them being designed for sequences of items. In this work, we study the problem of measuring the similarity ratio between sequences of itemsets. We present new combinatorial results for efficiently counting distinct and common subsequences. These theoretical results are the cornerstone for an effective dynamic programming approach to deal with this problem.</div>
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<name sortKey="Raissi, Chedy" sort="Raissi, Chedy" uniqKey="Raissi C" first="Chedy" last="Raïssi">Chedy Raïssi</name>
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<country name="Belgique"><noRegion><name sortKey="Calders, Toon" sort="Calders, Toon" uniqKey="Calders T" first="Toon" last="Calders">Toon Calders</name>
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