Collaborative fuzzy clustering algorithms: Some refinements and design guidelines

Luiz F.S. Coletta, Vendramin Lucas, Eduardo Raul Hruschka, Ricardo J.G.B. Campello, Witold Pedrycz

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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.

TidsskriftIEEE Transactions on Fuzzy Systems
Udgave nummer3
Sider (fra-til)444-462
StatusUdgivet - 2012
Udgivet eksterntJa

Bibliografisk note

Funding Information:
Manuscript received April 5, 2011; revised July 19, 2011 and October 13, 2011; accepted October 17, 2011. Date of publication November 9, 2011; date of current version May 27, 2012. This work was supported by the following Research Agencies: the Brazilian National Council for Scientific and Technological Development (CNPq) and the Foundation for Research Support of the State of Sao Paulo (FAPESP).


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