Abstract
GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known optima) of given clustering problems, which involve large data sets with many local optima. To circumvent this problem, we propose Niching Genetic K-means Algorithm (NGKA) that is based on modified deterministic crowding and embeds the computationally attractive k-means. Our experiments show that NGKA can consistently and efficiently identify high quality solutions. Experiments use both simulated and real data with varying size and varying number of local optima. The significance of NGKA is also shown on the experimental data sets by comparing through simulations with Genetically Guided Algorithm (GGA) and Genetic K-means Algorithm (GKA).
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References
Cho, R.J., et al.: A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell 2(1), 65–73 (1998)
Cucchiara, R.: Genetic Algorithms for Clustering in Machine Vision. Machine Vision and Applications 11(1), 1–6 (1998)
Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceeding of the 2nd Int. Conference on Genetic Algorithms, Hillsdale, New Jersey, pp. 41–49 (1987)
Hall, L.O., Ozyurt, B., Bezdek, J.C.: Clustering with a Genetically Optimized Approach. IEEE Transactions on Evolutionary Computation 3(2), 103–112 (1999)
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. Applied Statistics 28, 100–110 (1979)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Hruschka, E.R., Ebecken, N.F.F.: A genetic algorithm for cluster analysis. Intelligent Data Analysis 7, 15–25 (2003)
Iyer, V.R., et al.: The Transcriptional Program in the Response of Human Fibroblasts to Serum. Science 283, 83–87 (1999)
Krishna, K., Murty, M.: Narasimha: Genetic K-means Algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 29(3) (1999)
Mahfoud, S.W.: Niching methods for genetic algorithms. Ph.D. dissertation, Univ. of Illinois, Urbana-Champaign (1995)
Michalewicz, Z.: Genetic algorithms + Data structure = Evolution programs, 3rd edn. Springer, Heidelberg (1996)
Murthy, C.A., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recognition Letters 17, 825–832 (1996)
Petrowski, A.: A clearing procedure as a niching method for genetic algorithms. Proceeding of IEEE Int. Conf. Evolutionary Computation, 798-803 (1996)
Sarkar, M., Yegnanarayana, B., Khemani, D.: A Clustering Algorithm Using an Evolutionary Programming-based Approach. Pattern Recognition Lett. 18, 975–986 (1997)
Scheunders, P.: A genetic c-means clustering algorithm applied to color image quantization. Pattern Recognition 30(6), 859–866 (1997)
Syswerda, G.: A study of reproduction in generational and steady-state genetic algorithms. In: Foundations of Genetic Algorithms, pp. 94–101. Morgan Kauffmann Publishers, San Francisco (1991)
Yeung, K.Y.: Clustering analysis of gene expression data. PhD Thesis, University of Washington (2001)
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Sheng, W., Tucker, A., Liu, X. (2004). Clustering with Niching Genetic K-means Algorithm. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_15
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DOI: https://doi.org/10.1007/978-3-540-24855-2_15
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