Abstract
The current paper reports about the development of an automatic clustering technique which builds upon the search capability of a self-organizing multi-objective differential evolutionary approach. The algorithm utilizes new search operators which are developed after considering the neighbor-hood relationships of solutions of a population extracted using a self organizing map (SOM). Variable number of cluster centers are encoded in different solutions of the population which are evolved using the new search operators of differential evolution to automatically determine the number of clusters. Two cluster validity indices capturing different goodness measures of partitioning are used as objective functions. The effectiveness of the proposed framework namely, self organizing map based multi-objective (MO) clustering technique (SMEA_clust) is shown for automatically partitioning four artificial and four real-life data sets in comparison with a multi-objective differential evolution based clustering technique (similar to our proposed approach but without using SOM concept), two recent multi-objective clustering based techniques, VAMOSA and MOCK. Results are further validated using statistical significance tests.
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References
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc., Upper Saddle River (1988)
Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1650–1654 (2002)
Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New York (2005)
Bandyopadhyay, S., Maulik, U.: Nonparametric genetic clustering: comparison of validity indices. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 31(1), 120–125 (2001)
Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006)
Suresh, K., Kundu, D., Ghosh, S., Das, S., Abraham, A.: Data clustering using multi-objective differential evolution algorithms. Fund. Inform. 97(4), 381–403 (2009)
Kennedy, J.: Encyclopedia of machine learning. In: Saul, L., Fu, M.C. (eds.) Particle Swarm Optimization, pp. 760–766. Springer, New York (2011). doi:10.1007/978-1-4419-1153-7_200581
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Saha, S., Bandyopadhyay, S.: A symmetry based multiobjective clustering technique for automatic evolution of clusters. Pattern Recogn. 43(3), 738–751 (2010)
Saha, S., Bandyopadhyay, S.: A generalized automatic clustering algorithm in a multiobjective framework. Appl. Soft Comput. 13(1), 89–108 (2013)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
Zhang, H., Zhang, X., Gao, X.Z., Song, S.: Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble. Neurocomputing 173, 1868–1884 (2016)
Zhang, H., Zhou, A., Song, S., Zhang, Q., Gao, X.Z., Zhang, J.: A self-organizing multiobjective evolutionary algorithm. IEEE Trans. Evol. Comput. 20(5), 792–806 (2016)
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recogn. 37(3), 487–501 (2004)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan), 1–30 (2006)
Haykin, S.S.: Neural Networks and Learning Machines, vol. 3. Pearson, Upper Saddle River, NJ, USA (2009)
Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn. 35(6), 1197–1208 (2002)
Lichman, M.: UCI machine learning repository (2013)
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Saini, N., Chourasia, S., Saha, S., Bhattacharyya, P. (2017). A Self Organizing Map Based Multi-objective Framework for Automatic Evolution of Clusters. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_71
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DOI: https://doi.org/10.1007/978-3-319-70136-3_71
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