Abstract
We present a consensus-based algorithm to distributed fuzzy clustering that allows automatic estimation of the number of clusters. Also, a variant of the parallel Fuzzy c-Means algorithm that is capable of estimating the number of clusters is introduced. This variant, named DFCM, is applied for clustering data distributed across different data sites. DFCM makes use of a new, distributed version of the Xie-Beni validity criterion. Illustrative experiments show that for sites having data from different populations the developed consensus-based algorithm can provide better results than DFCM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Babuška, R.: Fuzzy Modeling For Control. Kluwer Academic, Dordrecht (1998)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms Pl.P (1981)
Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Math. Progr. 89, 149–185 (2000)
Campello, R.J.G.B., Hruschka, E.R.: A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets and Systems 157, 2858–2875 (2006)
Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons, Chichester (1999)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Trans. Fuzzy Systems 9, 595–607 (2001)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Systems 1, 98–110 (1993)
Loia, V., Pedrycz, W., Senatore, S.: Semantic web content analysis: A study in proximity-based collaborative clustering. IEEE Trans. Fuzzy Systems 15, 1294–1312 (2007)
Lux, M., Chatzichristofis, S.A.: Lire: Lucene image retrieval-An extensible java CBIR library. In: Proc. of the 16th ACM International Conference on Multimedia, pp. 1085–1088 (2008)
Pedrycz, W., Hirota, K.: A consensus-driven fuzzy clustering. Pattern Recognition Letters 29, 1333–1343 (2008)
Rahimi, S., Zargham, M., Thakre, A., Chhillar, D.: A parallel fuzzy c-mean algorithm for image segmentation. In: Fuzzy Information - Processing NAFIPS 2004, pp. 234–237 (2004)
Sledge, I.J., Bezdek, J.C., Havens, T.C., Keller, J.M.: Relational generalizations of cluster validity indices. IEEE Trans. Fuzzy Systems 18, 771–786 (2010)
Intelligent Sensory Information Systems, Amsterdam library of object images (aloi) (2010), http://staff.science.uva.nl/~aloi/
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 13, 841–847 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vendramin, L., Campello, R.J.G.B., Coletta, L.F.S., Hruschka, E.R. (2011). Distributed Fuzzy Clustering with Automatic Detection of the Number of Clusters. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19934-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-19934-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19933-2
Online ISBN: 978-3-642-19934-9
eBook Packages: EngineeringEngineering (R0)