WBBA-KM: A hybrid weight-based bat algorithm with the k-means algorithm for cluster analysis

Basit Öğe Kaydı
dc.contributor.authorIbrahim, Mohammed H.
dc.date.accessioned2024-02-23T14:49:28Z
dc.date.available2024-02-23T14:49:28Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractData clustering is an unsupervised classification method used to classify unlabeled objects into clusters. The clustering is performed by partitioning clustering, hierarchical clustering, fuzzy clustering, and density-based clustering methods. However, the center of the clusters is updated according to local searches with these traditional methods, and finding the best clusters center affects the clustering performance positively. In this study, a variant bat algorithm called weight-based bat algorithm (WBBA) is proposed and the proposed WBBA hybridized with the k-means clustering method (WBBA-KM) to determine the optimal centers of the clusters. The performance of the proposed WBBA-KM has been evaluated by using six different benchmark datasets from the UCI repository and the obtained results are compared with FCM, IFCM, KFCM, KIFCM, PSO-IFCM, GA-IFCM, ABC-IFCM, PSO-KIFCM, GA-KIFCM, ABC-KIFCM, and BA-KM clustering methods in the literature. According to the experimental results, the proposed WBBA-KM clustering method performed better performance from all other clustering methods in 4 of 6 benchmark datasets and achieved better performance from the BA-KM clustering method in all benchmark datasets.en_US
dc.identifier.endpage73en_US
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue1en_US
dc.identifier.startpage65en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12452/18209
dc.identifier.volume25en_US
dc.identifier.wosWOS:001001846700008en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal Of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBat Algorithmen_US
dc.subjectCluster Analysisen_US
dc.subjectOptimization Algorithmsen_US
dc.subjectUnsupervised Classificationen_US
dc.titleWBBA-KM: A hybrid weight-based bat algorithm with the k-means algorithm for cluster analysisen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu