Abstract
Clustering is primarily used to uncover the true underlying structure of a given data set. Most algorithms for fuzzy clustering often depend on initial guesses of the cluster centers and assumptions made as to the number of subgroups presents in the data. In this paper, we propose a method for fuzzy clustering without initial guesses on cluster number in the data set. Our method assumes that clusters will have the normal distribution. Our method can automatically estimates the cluster number and achieve the clustering according to the number, and it uses structured Genetic Algorithm (sGA) with graph structured chromosome.
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References
M. Holsheimer and A. Siebes, “Data mining: the search for knowledge in databases,” Report CS-R9406, The Netherlands, 1994.
U. Fayyad, G. P. Shapiro, P. Smyth and R. Uthurusamy, Advances in knowledge discovery and data mining, AAAI/MIT Press, Boston, MA, 1996.
W. Pedrycz, “Data mining and fuzzy modeling,” Proc. of IEEE Biennial Conference of the North American Fuzzy Information Processing Society — NAFIPS, pp.263–267, 1996.
J. C. Bezdek and S. K. Pal, Fuzzy models for pattern recognition, IEEE Press, 1992.
P. Cheeseman and J. Stutz, “Bayesian classification (AutoClass): Theory and results,” Advances in knowledge discovery and data mining, ed. U. M. Fayyad, pp.153–180, 1996.
R. P. Li and M. Mukaidono, “A maximum entropy approach to fuzzy clustering,” Proc. of the 4th IEEE Intern. Conf. on Fuzzy Systems (FUZZ IEEE/ IFES’95), Yokohama, Japan, March 20–24, pp.2227–2232, 1995.
T. Ohta, M. Nemoto, H. Ichihashi, T. Miyoshi, “Hard clustering by fuzzy c-means,” Journal of Japan Society for Fuzzy Theory and Systems, Vol. 10, No. 3, pp. 532–540, 1998, (in Japanese).
R. J. Bauer, Jr., Genetic algorithms and investment strategies, John Wiley & Sons Inc., N. York, 1994.
D. Dasgupta and D. R. McGregor, “Designing application-specific neural networks using the structured genetic algorithm,” in Proc. of COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks, Baltimore, Maryland, June 6, pp.87–96, 1992.
T. Kato and K. Ozawa, “Non-hierarchical clustering by a genetic algorithm,” Information processing society of Japan, Vol.37, No.1 1, pp.1950–1959, 1996, (in Japanese).
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© 1999 Springer-Verlag Berlin Heidelberg
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Imai, K., Kamiura, N., Hata, Y. (1999). An Unsupervised Clustering with Evolutionary Strategy to Estimate the Cluster Number. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_13
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DOI: https://doi.org/10.1007/3-540-48774-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66050-7
Online ISBN: 978-3-540-48774-6
eBook Packages: Springer Book Archive