Data clustering using multivariant optimization algorithm
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ORIGINAL ARTICLE
Data clustering using multivariant optimization algorithm Qin-Hu Zhang • Bao-Lei Li • Ya-Jie Liu Lian Gao • Lan-Juan Liu • Xin-Ling Shi
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Received: 1 January 2014 / Accepted: 18 August 2014 Springer-Verlag Berlin Heidelberg 2014
Abstract Data clustering is one of the most popular techniques in data mining to group data with great similarity and high dissimilarity into each cluster. This paper presents a new clustering method based on a novel heuristic optimization algorithm proposed recently and named as multivariant optimization algorithm (MOA) to locate the optimal solution automatically through global and local alternating search implemented by a global exploration group and several local exploitation groups. In order to demonstrate the performance of MOA-clustering method, it is applied to group six real-life datasets to obtain their clustering results, which may be compared with those received by employing K-means algorithm, genetic algorithm and particle swarm optimization. The results show that the proposed clustering algorithm is an effective and feasible method to reach a high accurate rate and stability in clustering problems. Keywords Data clustering Cluster centers Multivariant optimization algorithm Global and local optimization
1 Introduction Cluster analysis has become an important technique in exploratory data analysis, pattern recognition, machine learning, image segmentation, neural computing, and other engineering [1]. In the field of clustering, K-means
Q.-H. Zhang B.-L. Li Y.-J. Liu L. Gao L.-J. Liu X.-L. Shi (&) Department of Electronic Engineering, School of Information, Yunnan University, Kunming 650091, Yunnan Province, China e-mail: [email protected]; [email protected]
algorithm as a popular clustering method has been successfully applied to many practical clustering problems [2, 3]. However, the results obtained by using K-means algorithm may contain several local minima as the objective function of K-means which is not convex [4, 5]. Evolutionary algorithms such as genetic algorithm (GA) [6] and particle swarm optimization (PSO) [7] have been therefore introduced to solve such problems and widely applied to various clustering problems [8–17]. In this paper, the recently proposed discrete heuristic optimization algorithm named multivariant optimization algorithm (MOA) is adopted to solve clustering problems. In MOA, a search individual is named as an atom. The main idea of MOA is to search the solution space through alternating global–local search iterations where global exploration atoms explore the whole solution space to locate potential areas and then multiple local exploitation groups with different population are allotted to these potential areas for different levels of local exploitations. The better atoms generated in the optimization process are recorded in a data structure which is made up of a queue and some stacks, whereas the worse ones are extruded in competition. As to clustering problem, we can regard an array (atom) recording all
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