On Measuring Similarity for Sequences of Itemsets
Identifieur interne : 001911 ( Main/Exploration ); précédent : 001910; suivant : 001912On Measuring Similarity for Sequences of Itemsets
Auteurs : Elias Egho [France] ; Chedy Raïssi [France] ; Toon Calders [Belgique] ; Nicolas Jay [France] ; Amedeo Napoli [France]Source :
English descriptors
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 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. Experiments on healthcare trajectories and synthetic datasets, show that our measure of similarity produces competitive scores and indicates that our method is relevant for large scale sequential data analysis.
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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="IdRef">02636817X</idno><idno type="ISNI">0000000122597504</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></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>clustering</term><term>sequence mining</term><term>similarity measure</term></keywords></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 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. Experiments on healthcare trajectories and synthetic datasets, show that our measure of similarity produces competitive scores and indicates 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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