Abstract
Clustering belongs to the set of mathematical problems which aim at classification of data or objects into related sets or classes. Many different pattern clustering approaches based on the pattern membership model could be used to classify objects within various classes. Different models of Crisp, Hierarchical, Overlapping and Fuzzy clustering algorithms have been developed which serve different purposes. The main deficiency that most of the algorithms face is that the number of clusters for reaching the optimal arrangement is not automatically calculated and needs user intervention. In this paper we propose a fuzzy clustering technique (FACT) which determines the number of appropriate clusters based on the pattern essence. Different experiments for algorithm evaluation were performed which show a much better performance compared with the typical widely used K-means clustering algorithm.
Chapter PDF
Similar content being viewed by others
References
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. Halsted Press, New York (1993)
Hansen, P., Mladenovic, N.: J-Means: a new local search heuristic for minimum sum-of-squares clustering. Pattern Recognition 34(2), 405–413 (2001)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 2, pp. 281–297 (1967)
Sanjiv, K.B.: Adaptive K-Means Clustering. In: FLAIRS Conference 2004 (2004)
A survey of recent advances in hierarchical clustering algorithms. The Computer Journal 26(4), 354–359 (1983)
Barthélemy, J.P., Brucker, F.: NP-hard approximation problems in overlapping clustering. Journal of Classification 18, 159–183 (2001)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Guan, Y., Ghorbani, A., Belacel, N.: K-means+: An autonomous clustering algorithm (in submission)
Belacel, N., Hansen, P., Mladenovic, N.: Fuzzy J-means: A new heuristic for fuzzy clustering. Pattern Recognition 35, 2193–2200 (2002)
Bacao, F., Lobo, V., Painho, M.: Self-Organizing Maps as efficient initialization procedures and substitutes for k-means clustering. In: International Conference on Computational Science (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ensan, F., Yaghmaee, M.H., Bagheri, E. (2006). FACT: A New Fuzzy Adaptive Clustering Technique. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_79
Download citation
DOI: https://doi.org/10.1007/11758501_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
Online ISBN: 978-3-540-34380-6
eBook Packages: Computer ScienceComputer Science (R0)