Hybrid FCM-WOA Data Clustering Algorithm

dc.contributor.authorArslan, Hatice
dc.contributor.authorToz, Metin
dc.date.accessioned2020-05-01T12:10:25Z
dc.date.available2020-05-01T12:10:25Z
dc.date.issued2018
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.descriptionWOS: 000511448500024en_US
dc.description.abstractIn this work, we propose a hybrid clustering algorithm that integrates Fuzzy C-Means (FCM) and Whale Optimization Algorithm (WOA) using the Chebshev distance function. The FCM algorithm uses Euclidean distance to measure the similarity between the data. To avoid the existing disadvantages of the Euclidean distance, all distances in the FCM algorithm is calculated with the Chebsyhev distance function. The BOA algorithm is used to optimize the initial cluster centers. The proposed hybrid algorithm is tested with three different sets of data selected from UCI Machine Learning Repository database. As a result, it is seen that the clustering performance of the proposed algorithm is much better than the FCM algorithm.en_US
dc.description.sponsorshipIEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univen_US
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12684/6190
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 26Th Signal Processing And Communications Applications Conference (Siu)en_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFCMen_US
dc.subjectWOAen_US
dc.subjectChebsyheven_US
dc.subjectdistance functionen_US
dc.subjectdata clusteringen_US
dc.titleHybrid FCM-WOA Data Clustering Algorithmen_US
dc.typeConference Objecten_US

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