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.
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Evangelou IE, Hadjimitsis DG, Lazakidou AA, Clayton C (2001) Data mining and knowledge discovery in complex image data using artificial neural networks. Workshop on Complex Reasoning an Geographical Data, Cyprus
Selim SZ, Ismail MA (1984) K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Trans Pattern Anal Mach Intell 6:81–87
Hitendra Sarma T, Viswanath P, Eswara Reddy B (2013) A hybrid approach to speed-up the k-means clustering method. Int J Mach Learn Cybernet 4(2):107–117
Jinxin D, Minyong Qi (2009) A new algorithm for clustering based on particle swarm optimization and K-means. IEEE Int Conf Artif Intell Comput Intell 4:264–268
Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762
Filho JLR, Treleaven PC, Alippi C (1994) Genetic algorithm programming environments. IEEE Comput 27:28–43
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks (ICW). vol IV, Perth, Australia, pp 1942–1948
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3–2:95–99
Maulik U, Bandyopadhyay S (2000) Genetic algorithm based clustering technique. Pattern Recogn 33:1455–1465
Chiou YC, Lan LW (2001) Theory and methodology genetic clustering algorithms. Eur J Oper Res 135:413–427
Merwe VD, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation 2003 (CEC 2003), Canbella, Australia, pp 215–220
Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybernet 4(4):391–400
Cui XH, Potok TE, Palathingal P (2005) Document clustering using particle swarm optimization. IEEE swarm intelligence symposium 2005. Pasadena, California, pp 185–191
Chen CY, Ye F (2004) Particle swarm optimization algorithm and its application to clustering analysis. International conference on networking, sensing control Taipei, Taiwan, March 21–23
Omran M, Engelbrecht AP, Salman A (2005) Particle swarm optimization method for image clustering. Int J Pattern Recogn Artif Intell 19(3):297–322
Chuang LY, Hsiao CJ, Yang CH (2011) Chaotic particle swarm optimization for data clustering. Expert Syst Appl 38(12):14555–14563
Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings, evolutionary computation
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Zhang, QH., Li, BL., Liu, YJ. et al. Data clustering using multivariant optimization algorithm. Int. J. Mach. Learn. & Cyber. 7, 773–782 (2016). https://doi.org/10.1007/s13042-014-0294-5
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DOI: https://doi.org/10.1007/s13042-014-0294-5