Abstract
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.
Highlights
Due to the increasingly widespread digitalization processes at global and local level, large-scale data are obtained in many different fields
It is clearly seen that the clustering results obtained in the original Whale Optimization Algorithm (WOA) algorithm are improved in the WOA-Levy flight (LF) hybrid method
The results show that the WOA generally has better results than other three algorithms and by using the Levy flight strategy the performance of the WOA has been improved
Summary
Due to the increasingly widespread digitalization processes at global and local level, large-scale data are obtained in many different fields. It is very important to process the data accurately and quickly in order to make more effective use of the large-scale data and extract meaningful information. For this reason, new methods are constantly being developed for the efficient use of knowledge in many areas such as industry, banking, marketing, medicine, engineering and economics, where data mining techniques are being applied. With the ever-developing science and technology, optimization problems are increasing These problems are considered to be more complex and difficult because they are large-scale and have many factors. When compared with its components a hybrid algorithm generally has a stronger and robust structure and can effectively solve complex optimization problems [1,2,3]
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