Cluster analysis has been playing an important role in pattern recognition, image processing, and time series analysis. The majority of the existing clustering algorithms depend on initial parameters and assumptions about the underlying dat,a structure. In this paper a fuzzy method of mode separation is proposed. The method addresses the task of multi-modal partition through a sequence of locally sensitive searches guided by a stochastic gradient ascent procedure, and addresses the cluster validity problem through a global partition performance criterion. the algorithm is computational efficient and provided gocd results when tested with a number of simulated and real data sets.
clustering analysis, fuzzy clustering, mode separation, cluster validity
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