A SINGLE-STEP CLUSTERING ALGORITHM BASED ON A NEW INFORMATION-THEORETIC SAMPLE ASSOCIATION METRIC DEFINITION

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dc.contributor.authorTemel, Turgay
dc.date.accessioned2021-03-20T20:14:07Z
dc.date.available2021-03-20T20:14:07Z
dc.date.issued2017
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractA single-step information-theoretic algorithm that is able to identify possible clusters in dataset is presented. The proposed algorithm consists in representation of data scatter in terms of similarity-based data point entropy and probability descriptions. By using these quantities, an information-theoretic association metric called mutual ambiguity between data points is defined, which then is to be employed in determining particular data points called cluster identifiers. For forming individual clusters corresponding to cluster identifiers determined as such, a cluster relevance rule is defined. Since cluster identifiers and associative cluster member data points can be identified without recursive or iterative search, the algorithm is single-step. The algorithm is tested and justified with experiments by using synthetic and anonymous real datasets. Simulation results demonstrate that the proposed algorithm also exhibits more reliable performance in statistical sense compared to major algorithms.en_US
dc.identifier.doi10.14311/NNW.2017.27.027en_US
dc.identifier.endpage528en_US
dc.identifier.issn1210-0552
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage519en_US
dc.identifier.urihttp://doi.org/10.14311/NNW.2017.27.027
dc.identifier.urihttps://hdl.handle.net/20.500.12885/999
dc.identifier.volume27en_US
dc.identifier.wosWOS:000416417400004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTemel, Turgay
dc.language.isoenen_US
dc.publisherAcad Sciences Czech Republic, Inst Computer Scienceen_US
dc.relation.ispartofNeural Network Worlden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectclusteringen_US
dc.subjectclustering algorithmsen_US
dc.subjectinformation theoryen_US
dc.subjectmutual informationen_US
dc.subjectunsupervised learningen_US
dc.titleA SINGLE-STEP CLUSTERING ALGORITHM BASED ON A NEW INFORMATION-THEORETIC SAMPLE ASSOCIATION METRIC DEFINITIONen_US
dc.typeArticleen_US

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