Arslan, HaticeToz, Metin2020-05-012020-05-012018978-1-5386-1501-02165-0608https://hdl.handle.net/20.500.12684/619026th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYWOS: 000511448500024In 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.trinfo:eu-repo/semantics/closedAccessFCMWOAChebsyhevdistance functiondata clusteringConference ObjectN/A