A new approach on search for similar documents with multiple categories using fuzzy clustering

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dc.contributor.authorSaracoglu, Ridvan
dc.contributor.authorTuetuencue, Kemal
dc.contributor.authorAllahverdi, Novruz
dc.date.accessioned2020-03-26T17:26:19Z
dc.date.available2020-03-26T17:26:19Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSearching for similar document has an important role in text mining and document management. In whether similar document search or in other text mining applications generally document classification is focused and class or category that the documents belong to is tried to be determined. The aim of the present study is the investigation of the case which includes the documents that belong to more than one category. The system used in the present study is a similar document search system that uses fuzzy clustering. The situation of belonging to more than one category for the documents is included by this system. The proposed approach consists of two stages to solve multicategories problem. The first stage is to find out the documents belonging to more than one category. The second stage is the determination of the categories to which these found documents belong to. For these two aims alpha-threshold Fuzzy Similarity Classification Method (alpha-FSCM) and Multiple Categories Vector Method (MCVM) are proposed as written order. Experimental results showed that proposed system can distinguish the documents that belong to more than one category efficiently. Regarding to the finding which documents belong to which classes, proposed system has better performance and success than the traditional approach. (c) 2007 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2007.04.003en_US
dc.identifier.endpage2554en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2545en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2007.04.003
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22158
dc.identifier.volume34en_US
dc.identifier.wosWOS:000253521900031en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjecttext miningen_US
dc.subjectdocument similarityen_US
dc.subjectsimilarity searchen_US
dc.subjectfuzzy clusteringen_US
dc.subjectmultiple categoriesen_US
dc.titleA new approach on search for similar documents with multiple categories using fuzzy clusteringen_US
dc.typeArticleen_US

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