期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 20, 期 3, 页码 444-462出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2011.2175400
关键词
Collaborative and parallel fuzzy clustering; design and selection guidelines; distributed knowledge discovery; validity indices
资金
- Brazilian National Council for Scientific and Technological Development (CNPq)
- Foundation for Research Support of the State of Sao Paulo (FAPESP)
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.
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