Abstract
Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means (FCM) is one of the most popular clustering methods based on minimization of a criterion function because it works fast in most situations. However, it is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) and differential evolution (DE) are two promising algorithms for numerical optimization. Two hybrid data clustering algorithms based the two evolution algorithms and the FCM algorithm, called HPSOFCM and HDEFCM respectively, are proposed in this research. The hybrid clustering algorithms make full use of the merits of the evolutionary algorithms and the FCM algorithm. The performances of the HPSOFCM algorithm and the HDEFCM algorithm are compared with those of the FCM algorithm on six data sets. Experimental results indicate the HPSOFCM algorithm and the HDEFCM algorithm can help the FCM algorithm escape from local optima.
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References
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Klawonn, F., Keller, A.: Fuzzy Clustering with Evolutionary Algorithms. Int.J. of Intelligent Systems. 13(10-11), 975–991 (1998)
Mehdizadeh, E., Tavakkoli-Moghaddam, R.: A Hybrid Fuzzy Clustering PSO Algorithm for a Clustering Supplier Problem. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1466–1470 (2007)
Das, S., Konar, A., Chakraborty, U.K.: Automatic Fuzzy Segmentation of Images with Differential Evolution. In: IEEE Congress on Evolutionary Computation, pp. 2026–2033 (2006)
Berget, I., Mevik, B., Næs, T.: New Modiffcations and Applications of Fuzzy C-means Methodology. Computational Statistics and Data Analysis 52, 2403–2418 (2008)
Wang, X., Wang, Y., Wang, L.: Improving Fuzzy C-means Clustering Based on Feature-weight Learning. Pattern Recognition Letters 25, 1123–1132 (2004)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE intenational conference on neural networks, pp. 1942–1948. IEEE Press, New Jersey (1995)
Ali, M.M., Kaelo, P.: Improved Particle Swarm Algorithms for Global Optimization. Applied Mathematics and Computation 196, 578–593 (2008)
Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, University of California. ICSI, Berkeley (1995)
Huang, F., Wang, L., He, Q.: An Effective Co-evolutionary Differential Evolution for Constrained Optimization. Applied Mathematics and Computation 186, 340–356 (2007)
Qian, W., Li, A.: Adaptive Differential Evolution Algorithm for Multiobjective Optimization Problems. Applied Mathematics and Computation 201, 431–440 (2008)
Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)
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Yang, F., Zhang, C., Sun, T. (2009). Particle Swarm Optimization and Differential Evolution in Fuzzy Clustering. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_61
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DOI: https://doi.org/10.1007/978-3-642-03040-6_61
Publisher Name: Springer, Berlin, Heidelberg
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