Toz, GülizErdoğmuş, Pakize2020-05-012020-05-012016978-1-5090-1679-2https://hdl.handle.net/20.500.12684/604424th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEYWOS: 000391250900014Clustering is dividing a dataset into subsets that has similar characteristics. In this study, fuzzy c-means clustering algorithm (FCM) and a new evolutionary optimization algorithm, Backtracking Search (BSA) algorithm, were combined and a new hybrid clustering algorithm (BSAFCM) was proposed. Moreover, the local search abilities of the new algorithm was improved and the new algorithm was named as g-BSAFCM. Three benchmark datasets from UCI Machine Learning Repository database were clustered by using the developed algorithms and FCM. According to the results g-BSAFCM has achieved better results than FCM and BSAFCM.trinfo:eu-repo/semantics/closedAccessclusteringBSAFCMkey wordsConference Object145148N/A