Data clustering using Lévy flight and local memory bees algorithm
by Nahlah M. Shatnawi
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 12, No. 1, 2017

Abstract: One of the most popular clustering algorithms is K- means cluster due to its simplicity and efficiency. Although clustering using K-means algorithm is fast and produces good results, it still has a number of limitations including initial centroid selection and local optima. The purpose of this research is to develop a hybrid algorithm that address k-means clustering limitations and improve its performance by finding optimal cluster centre. In this paper, Lévy-flights or Lévy motion is one of non-Gaussian random processes used to solve the initial centroid problem. Bees algorithm is a population-based algorithm which has been proposed to overcome the local optima problem, used along with its local memory to enhance the efficiency of K-means. The proposed algorithm applied to different datasets and compared with K-means and basic Bees algorithm. The results show that the proposed algorithm gives better performance and avoid local optima problem.

Online publication date: Tue, 07-Mar-2017

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