Some Recent Investigations of a New Fuzzy Partitioning Algorithm and its Application to Pattern Classification Problems

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Abstract
Recent results pertaining to a newly developed fuzzy partitioning algorithm are surveyed. The algorithm has potential value as a heuristic tool for identifying clusters within large finite data sets, and more specifically, for estimating the parameters in a mixture of unimodal probability densities, given a finite sample drawn from the mixture. The topics treated are: fuzzy partitions, conventional clustering algorithms, fuzzy clustering algorithms, asymptotic behavior of optimal fuzzy partitions with increasing cluster separation, scalar measures of partition fuzziness, and unsupervised learning and parameter estimation.

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