On-line evolving fuzzy clustering
Ravi, V.; Srinivas, E.; Kasabov, N
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In this paper, a novel on-line evolving fuzzy clustering method that extends the evolving clustering method (ECM) of Kasabov and Song (2002) is presented, called EFCM. Since it is an on-line algorithm, the fuzzy membership matrix of the data is updated whenever the existing cluster expands, or a new cluster is formed. EFCM does not need the numbers of the clusters to be pre-defined. The algorithm is tested on several benchmark data sets, such as Iris, Wine, Glass, E-Coli, Yeast and Italian Olive oils. EFCM results in the least objective function value compared to the ECM and Fuzzy C-Means. It is significantly faster (by several orders of magnitude) than any of the off-line batch-mode clustering algorithms. A methodology is also proposed for using theXie-Beni cluster validity measure to optimize the number of clusters. © 2007 IEEE.