Distributed fuzzy clustering with automatic detection of the number of clusters

Vendramin, L., Campello, R.J.G.B., Coletta, L.F.S., and Hruschka, E.R. (2011) Distributed fuzzy clustering with automatic detection of the number of clusters. In: Advances in Intelligent and Soft Computing (91), pp. 1-8. From: 2011 International Symposium on Distributed Computing and Artificial Intelligence, 6-8 April 2011, Salamanca, Spain.

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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.

Item ID: 47607
Item Type: Conference Item (Research - E1)
ISBN: 978-3-642-19933-2
Keywords: clustering data, distributed clustering, consensus clustering
Funders: CNPq, Brazil, FAPESP
Date Deposited: 08 Mar 2017 07:40
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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