FCA and pattern structures for mining care trajectories
Identifieur interne : 001082 ( Main/Exploration ); précédent : 001081; suivant : 001083FCA and pattern structures for mining care trajectories
Auteurs : Aleksey Buzmakov [France] ; Elias Egho [France] ; Nicolas Jay [France] ; Sergei O. Kuznetsov [Russie] ; Amedeo Napoli [France] ; Chedy Raïssi [France]Source :
English descriptors
Abstract
In this paper, we are interested in the analysis of sequential data and we propose an original framework based on Formal Concept Analysis (FCA). For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Pattern structures are used in FCA for dealing with complex data such as intervals or graphs. Here they are adapted to sequences. For that, we introduce a subsumption operation for sequence comparison, based on subsequence matching. Then, a projection, i.e. a kind of data reduction of sequential pattern structures, is suggested in order to increase the eficiency of the approach. Finally, we discuss an application to a dataset including patient trajectories (the motivation of this work), which is a sequential dataset and can be processed with the introduced framework. This research work provides a new and eficient extension of FCA to deal with complex (not binary) data, which can be an alternative to the analysis of sequential datasets
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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><author><name sortKey="Raissi, Chedy" sort="Raissi, Chedy" uniqKey="Raissi C" first="Chedy" last="Raïssi">Chedy Raïssi</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-205125" status="VALID"><idno type="RNSR">200818291B</idno><orgName>Knowledge representation, reasonning</orgName><orgName type="acronym">ORPAILLEUR</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.inria.fr/equipes/orpailleur</ref></desc><listRelation><relation active="#struct-129671" type="direct"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-423086" type="direct"></relation><relation active="#struct-206040" type="indirect"></relation><relation active="#struct-413289" type="indirect"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation></listRelation><tutelles><tutelle active="#struct-129671" type="direct"><org type="laboratory" xml:id="struct-129671" status="VALID"><idno type="RNSR">198618246Y</idno><orgName>INRIA Nancy - Grand Est</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/nancy</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName><orgName type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-423086" type="direct"><org type="department" xml:id="struct-423086" status="VALID"><orgName>Department of Natural Language Processing & Knowledge Discovery</orgName><orgName type="acronym">LORIA - NLPKD</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.loria.fr/la-recherche-en/departements/Knowledge-and-Language-Management</ref></desc><listRelation><relation active="#struct-206040" type="direct"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-413289" type="indirect"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation></listRelation></org></tutelle><tutelle active="#struct-206040" type="indirect"><org type="laboratory" xml:id="struct-206040" status="VALID"><idno type="IdRef">067077927</idno><idno type="RNSR">198912571S</idno><idno type="IdUnivLorraine">[UL]RSI--</idno><orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName><orgName type="acronym">LORIA</orgName><date type="start">2012-01-01</date><desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.loria.fr</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation><relation active="#struct-413289" type="direct"></relation><relation name="UMR7503" active="#struct-441569" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - 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></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>formal concept analysis</term><term>pattern structures</term><term>sequences</term><term>sequential pattern structures</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we are interested in the analysis of sequential data and we propose an original framework based on Formal Concept Analysis (FCA). For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Pattern structures are used in FCA for dealing with complex data such as intervals or graphs. Here they are adapted to sequences. For that, we introduce a subsumption operation for sequence comparison, based on subsequence matching. Then, a projection, i.e. a kind of data reduction of sequential pattern structures, is suggested in order to increase the eficiency of the approach. Finally, we discuss an application to a dataset including patient trajectories (the motivation of this work), which is a sequential dataset and can be processed with the introduced framework. This research work provides a new and eficient extension of FCA to deal with complex (not binary) data, which can be an alternative to the analysis of sequential datasets</div></front></TEI><affiliations><list><country><li>France</li><li>Russie</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="Buzmakov, Aleksey" sort="Buzmakov, Aleksey" uniqKey="Buzmakov A" first="Aleksey" last="Buzmakov">Aleksey Buzmakov</name></region><name sortKey="Egho, Elias" sort="Egho, Elias" uniqKey="Egho E" first="Elias" last="Egho">Elias Egho</name><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="Russie"><noRegion><name sortKey="Kuznetsov, Sergei O" sort="Kuznetsov, Sergei O" uniqKey="Kuznetsov S" first="Sergei O." last="Kuznetsov">Sergei O. Kuznetsov</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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