Abstract
The minimum sum of squares clustering problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this article, a recent metaheuristic technique, the noising method, is introduced to explore the proper clustering of data sets under the criterion of minimum sum of squares clustering. Meanwhile, K-means algorithm as a local improvement operation is integrated into the noising method to improve the performance of the clustering algorithm. Extensive computer simulations show that the proposed approach is feasible and effective.
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References
Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, New Jersey (1988)
Selim, S.Z., Ismail, M.A.: K-means-type algorithm: generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 81–87 (1984)
Murthy, C.A., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recognition Letters 17, 825–832 (1996)
Al-sultan, K.S.: A tabu search approach to the clustering problem. Pattern Recognition 28, 1443–1451 (1995)
Bandyopadhyay, S., Maulik, U., Pakhira, M.K.: Clustering using simulated annealing with probabilisitc redistribution. International Journal of Pattern Recognition and Artificial Intelligence 15, 269–285 (2001)
Charon, I., Hudry, O.: The noising method: a new method for combinatorial optimization. Operations Research Letters 14, 133–137 (1993)
Charon, I., Hudry, O.: The noising method: a generalization of some metaheuristics. European Journal of Operational Research 135, 86–101 (2001)
Chelouah, R., Siarry, P.: A hybrid method combining continuous tabu search and Nelder– Mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research 161, 636–654 (2005)
Fisher, R.A.: The use of multiple measurements in taxonomic problem. Annals of Eugenics 7, 179–188 (1936)
Johnson, R.A., Wichern, D.W.: Applied multivariate statistical analysis. Prentice-Hall, New Jersey (1982)
Pal, S.K., Majumder, D.D.: Fuzzy sets and decision making approaches in vowel and speaker recognition. IEEE Transactions on System, Man and Cybernetics SMC-7, 625–629 (1977)
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Liu, Y., Liu, Y., Chen, K. (2005). Clustering with Noising Method. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_25
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DOI: https://doi.org/10.1007/11527503_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
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