Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines

• Published in: IEEE transactions on fuzzy systems Vol. 20; no. 3; pp. 444 - 462
• Main Authors: Coletta, L. F. S., Vendramin, L., Hruschka, E. R., Campello, R. J. G. B., Pedrycz, W.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Clustering; Clustering algorithms; Clusters; Collaboration; Collaborative and parallel fuzzy clustering; design and selection guidelines; Distributed databases; distributed knowledge discovery; Fuzzy; Fuzzy logic; Fuzzy set theory; Guidelines; Indexes; Partitioning algorithms; Prototypes; Series (mathematics); Studies; validity indices
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2011.2175400

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.