An application of the whale optimization algorithm with Levy flightstrategy for clustering of medical datasets

Abstract

Clustering, which is handled by many researchers, is separating data into clusterswithout supervision. In clustering, the data are grouped using similarities ordifferences between them. Many traditional and heuristic algorithms are used inclustering problems and new techniques continue to be developed today. In thisstudy, a new and effective clustering algorithm was developed by using the WhaleOptimization Algorithm (WOA) and Levy flight (LF) strategy that imitates thehunting behavior of whales. With the developed WOA-LF algorithm, clusteringwas performed using ten medical datasets taken from the UCI Machine LearningRepository database. The clustering performance of the WOA-LF was comparedwith the performance of k-means, k-medoids, fuzzy c-means and the originalWOA clustering algorithms. Application results showed that WOA-LF has moresuccessful clustering performance in general and can be used as an alternativealgorithm in clustering problems.