Collaborative fuzzy clustering algorithms: some refinements and design guidelines

Coletta, Luiz F.S., Vendramin, Lucas, Hruschka, Eduardo Raul, Campello, Ricardo J.G.B., and Pedrycz, Witold (2012) Collaborative fuzzy clustering algorithms: some refinements and design guidelines. IEEE Transactions on Fuzzy Systems, 20 (3). pp. 444-462.

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Abstract

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

Item ID: 47069
Item Type: Article (Research - C1)
ISSN: 1941-0034
Keywords: collaborative and parallel fuzzy clustering, design and selection guidelines, distributed knowledge discovery, validity indices
Date Deposited: 04 Jan 2017 08:04
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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