Finding number of clusters in single-step with similarity-based information-theoretic algorithm

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dc.contributor.authorTemel, Turgay
dc.date.accessioned2021-03-20T20:15:39Z
dc.date.available2021-03-20T20:15:39Z
dc.date.issued2014
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractA single-step algorithm is presented to find the number of clusters in a dataset. An almost two-valued function called cluster-boundary indicator is introduced with the use of similarity-based information-theoretic sample entropy and probability descriptions. This function finds inter-cluster boundary samples for cluster availability in a single iteration. Experiments with synthetic and anonymous real datasets show that the new algorithm outperforms its major counterparts statistically in terms of time complexity and the number of clusters found successfully.en_US
dc.identifier.doi10.1049/el.2013.3362en_US
dc.identifier.endpageU34en_US
dc.identifier.issn0013-5194
dc.identifier.issn1350-911X
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage29en_US
dc.identifier.urihttp://doi.org/10.1049/el.2013.3362
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1219
dc.identifier.volume50en_US
dc.identifier.wosWOS:000328703900012en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTemel, Turgay
dc.language.isoenen_US
dc.publisherInst Engineering Technology-Ieten_US
dc.relation.ispartofElectronics Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcomputational complexityen_US
dc.subjectentropyen_US
dc.subjectpattern clusteringen_US
dc.subjectprobabilityen_US
dc.subjectstatistical analysisen_US
dc.subjectsingle-step algorithmen_US
dc.subjecttwo-valued functionen_US
dc.subjectcluster-boundary indicatoren_US
dc.subjectprobability descriptionsen_US
dc.subjectintercluster boundaryen_US
dc.subjectcluster availabilityen_US
dc.subjectsynthetic data setsen_US
dc.subjectreal data setsen_US
dc.subjectstatistical analysisen_US
dc.subjecttime complexityen_US
dc.subjectsimilarity-based information-theoretic sample entropyen_US
dc.titleFinding number of clusters in single-step with similarity-based information-theoretic algorithmen_US
dc.typeArticleen_US

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