Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Keywords: Data Clustering, Fuzzy C-means, Grey Wolf Optimization (GWO), K-means, K-medoids

Abstract

The clustering which is an unsupervised classification method is very important for data processing applications. The main purpose of the clustering is to separate the data samples into different groups by using the similarity (or dissimilarity) between data samples. There are many conventional and heuristic algorithms which are used for the clustering problem. Nevertheless, in last years, it is seen that many new techniques are proposed and improved to solve the clustering problem. In this paper, grey wolf optimization (GWO) algorithm which is modelled according to the social behaviour of grey wolves is applied to partition the data samples by searching the optimal center of the clusters. The clustering performance of the GWO is compared with the performances of the three clustering algorithms: k-means, k-medoids and fuzzy c-means algorithms. The experiments show that the GWO algorithm has generally better results than the other clustering algorithms and can be alternatively applied on the clustering problem.

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Published
2019-12-12
How to Cite
[1]
M. KARAKOYUN, O. INAN, and İhtisam AKTO, “Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem”, IJISAE, vol. 7, no. 4, pp. 201-206, Dec. 2019.
Section
Research Article