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Vers une mesure de similarité pour les séquences complexes

Elias Egho 1 Chedy Raïssi 1 Toon Calders 2 Nicolas Jay 1 Amedeo Napoli 1 
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
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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Submitted on : Saturday, November 9, 2013 - 11:29:12 PM
Last modification on : Friday, August 12, 2022 - 3:50:27 AM
Long-term archiving on: : Monday, February 10, 2014 - 12:00:21 PM


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  • HAL Id : hal-00885965, version 1


Elias Egho, Chedy Raïssi, Toon Calders, Nicolas Jay, Amedeo Napoli. Vers une mesure de similarité pour les séquences complexes. Extraction et gestion des connaissances (EGC'2013), Jan 2013, Toulouse, France. pp.335-340. ⟨hal-00885965⟩



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