Optimization of fuzzy clustering criteria using genetic algorithms

This paper introduces a general approach based on genetic algorithms for optimizing a broad class of clustering criteria. The standard approach for optimizing these criteria has been to alternate optimizations between the variables which represent fuzzy memberships of the data to various clusters, and those prototype variables which determine the geometry of the clusters. The approach suggested here first re-parameterizes the criteria into functions of the prototype variables alone. The prototype variables are then coded as binary strings so that genetic algorithms can be applied. An overview of the approach and two simple numerical examples are given