An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets
Küçük Resim Yok
Tarih
2021
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.
Açıklama
Anahtar Kelimeler
Clustering, Whale Optimization Algorithm, Levy Flight, K-Means, K-Medoids, Fuzzy C-Means
Kaynak
International Journal Of Optimization And Control-Theories & Applications-Ijocta
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
11
Sayı
2
