Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction
Identifieur interne : 005032 ( Hal/Checkpoint ); précédent : 005031; suivant : 005033Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction
Auteurs : Hacène Cherfi [France] ; Amedeo Napoli [France] ; Yannick Toussaint [France]Source :
Descripteurs français
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Abstract
This paper proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect to a set of quality measures and domain knowledge, under the control of an analyst. The article includes an experimentation on a real-world text corpus holding on molecular biology.
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Hal:inria-00107723Le document en format XML
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type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</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/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>association rules</term><term>biologie moléculaire</term><term>fouille de textes</term><term>mesures de qualité</term><term>molecular biology</term><term>quality measures</term><term>règles d'association</term><term>text mining</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect to a set of quality measures and domain knowledge, under the control of an analyst. The article includes an experimentation on a real-world text corpus holding on molecular biology.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction</title><author role="aut"><persName><forename type="first">Hacène</forename><surname>Cherfi</surname></persName><email>Hacne.Cherfi@loria.fr</email><idno type="halauthor">138770</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Amedeo</forename><surname>Napoli</surname></persName><email></email><idno type="halauthor">122559</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Yannick</forename><surname>Toussaint</surname></persName><email></email><idno type="halauthor">130218</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2358"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-10-19 09:07:02</date><date type="whenModified">2016-05-18 08:57:20</date><date type="whenReleased">2006-10-20 15:32:31</date><date type="whenProduced">2003-09</date><date type="whenEndEmbargoed">2015-02-10</date><ref type="file" target="https://hal.inria.fr/inria-00107723/document"><date notBefore="2015-02-10"></date></ref><ref type="file" n="1" target="https://hal.inria.fr/inria-00107723/file/A03-R-417.pdf"><date notBefore="2015-02-10"></date></ref></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00107723</idno><idno type="halUri">https://hal.inria.fr/inria-00107723</idno><idno type="halBibtex">cherfi:inria-00107723</idno><idno type="halRefHtml">M. Nadif, A. Napoli, E. SanJuan, A. Sigayret. Journées d'informatique Messine - JIM'03, Sep 2003, Metz, France, INRIA Lorraine, pp.285--294, 2003</idno><idno type="halRef">M. Nadif, A. Napoli, E. SanJuan, A. Sigayret. Journées d'informatique Messine - JIM'03, Sep 2003, Metz, France, INRIA Lorraine, pp.285--294, 2003</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes et comité de lecture. nationale.</note><note type="audience" n="3">National</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction</title><author role="aut"><persName><forename type="first">Hacène</forename><surname>Cherfi</surname></persName><email>Hacne.Cherfi@loria.fr</email><idno type="halAuthorId">138770</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Amedeo</forename><surname>Napoli</surname></persName><idno type="halAuthorId">122559</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Yannick</forename><surname>Toussaint</surname></persName><idno type="halAuthorId">130218</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2358"></affiliation></author></analytic><monogr><idno type="localRef">A03-R-417 || cherfi03c</idno><meeting><title>Journées d'informatique Messine - JIM'03</title><date type="start">2003-09</date><settlement>Metz, France</settlement></meeting><respStmt><resp>conferenceOrganizer</resp><name>E. SanJuan</name></respStmt><editor>M. Nadif, A. Napoli, E. SanJuan, A. Sigayret</editor><imprint><publisher>INRIA Lorraine</publisher><biblScope unit="pp">285--294</biblScope><date type="datePub">2003-09</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">règles d'association</term><term xml:lang="fr">molecular biology</term><term xml:lang="fr">association rules</term><term xml:lang="fr">text mining</term><term xml:lang="fr">quality measures</term><term xml:lang="fr">fouille de textes</term><term xml:lang="fr">mesures de qualité</term><term xml:lang="fr">biologie moléculaire</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">This paper proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect to a set of quality measures and domain knowledge, under the control of an analyst. The article includes an experimentation on a real-world text corpus holding on molecular biology.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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