Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines, by L. F. S. Coletta, L. Vendramin, E. R. Hruschka, R. J. G. B. Campello and W. Pedrycz, IEEE Transactions on Fuzzy Systems, Vol. 20, No. 3, June 2012, pp. 444 - 462.

Digital Object Identifier: 10.1109/TFUZZ.2011.2175400

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6074934

“There are some variants of widely used Fuzzy C-Means (FCM) algorithm which support clustering data distributed across different sites. Those methods have been researched under different names, for instance, collaborative and parallel fuzzy clustering. This paper performs augmentation of two FCM-based clustering algorithms used to cluster distributed data by the following ways: arriving at some constructive ways of determining essential parameters of the algorithms and forming a set of systematically structured guidelines. Two examples to illustrated the second means, that is,  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 complete and complexity analysis is reported, including space, time and communication aspects. In addition, a series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.”