An efficient hybrid approach based on K-means and generalized fashion algorithms for cluster analysis

Abstract

Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. The k-means algorithm is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. The Fashion Algorithm is one effective method for searching problem space to find a near optimal solution. This paper presents a hybrid optimization algorithm based on Generalized Fashion Algorithm and k-means for optimum clustering. The new algorithm is tested on several data sets and its performance is compared with those of Generalized Fashion Algorithm, particle swarm optimization, imperialist competitive algorithm, genetic algorithm and k-means algorithm. The experimental results are encouraging in term of the quality of the solutions and the convergence speed of the proposed algorithm.