Title: Data clustering using Lévy flight and local memory bees algorithm

Authors: Nahlah M. Shatnawi

Addresses: Department of Computer Science, Yarmouk University, Irbid, Jordan

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.

Keywords: data clustering; k-means clustering; Levy flight; bees algorithm; initial centroid selection; local optima; optimal cluster centre; Levy motion.

DOI: 10.1504/IJBIDM.2017.082706

International Journal of Business Intelligence and Data Mining, 2017 Vol.12 No.1, pp.14 - 24

Received: 19 Jun 2016
Accepted: 01 Dec 2016
Published online: 07 Mar 2017 *

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