On measuring similarity for sequences of itemsets
Identifieur interne : 000554 ( Main/Exploration ); précédent : 000553; suivant : 000555On measuring similarity for sequences of itemsets
Auteurs : Elias Egho [France] ; Chedy Raïssi [France] ; Toon Calders [Belgique] ; Nicolas Jay [France] ; Amedeo Napoli [France]Source :
- Data Mining and Knowledge Discovery [ 1384-5810 ] ; 2015-05-01.
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 between sequences of itemsets. We focus on the notion of common subsequences as a way to measure similarity between a pair of sequences composed of a list of itemsets. We present new combinatorial results for efficiently counting distinct and common subsequences. These theoretical results are the cornerstone of an effective dynamic programming approach to deal with this problem. In addition, we propose an approximate method to speed up the computation process for long sequences. We have applied our method to various data sets: healthcare trajectories, online handwritten characters and synthetic Responsible editors: 123 Elias Egho et al. data. Our results confirm that our measure of similarity produces competitive scores and indicate that our method is relevant for large scale sequential data analysis.
Url:
DOI: 10.1007/s10618-014-0362-1
Affiliations:
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Le document en format XML
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CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1007/s10618-014-0362-1</idno><series><title level="j">Data Mining and Knowledge Discovery</title><idno type="ISSN">1384-5810</idno><imprint><date type="datePub">2015-05-01</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><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 between sequences of itemsets. We focus on the notion of common subsequences as a way to measure similarity between a pair of sequences composed of a list of itemsets. We present new combinatorial results for efficiently counting distinct and common subsequences. These theoretical results are the cornerstone of an effective dynamic programming approach to deal with this problem. In addition, we propose an approximate method to speed up the computation process for long sequences. We have applied our method to various data sets: healthcare trajectories, online handwritten characters and synthetic Responsible editors: 123 Elias Egho et al. data. Our results confirm that our measure of similarity produces competitive scores and indicate that our method is relevant for large scale sequential data analysis.</div></front></TEI><affiliations><list><country><li>Belgique</li><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Egho, Elias" sort="Egho, Elias" uniqKey="Egho E" first="Elias" last="Egho">Elias Egho</name></region><name sortKey="Jay, Nicolas" sort="Jay, Nicolas" uniqKey="Jay N" first="Nicolas" last="Jay">Nicolas Jay</name><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name><name sortKey="Raissi, Chedy" sort="Raissi, Chedy" uniqKey="Raissi C" first="Chedy" last="Raïssi">Chedy Raïssi</name></country><country name="Belgique"><noRegion><name sortKey="Calders, Toon" sort="Calders, Toon" uniqKey="Calders T" first="Toon" last="Calders">Toon Calders</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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