On the optimal choice of parameters in a fuzzy c-means algorithm

The authors propose a technique for determining the weighting exponent, m, a parameter in a fuzzy c-means algorithm, using the concept of fuzzy decision theory. They define a fuzzy goal as maximizing the number of data points in a cluster and a fuzzy constraint as the minimizing of the sum of square errors within a cluster. A decision about m is made by taking the intersection of the fuzzy goal and constraint such that given m, the fuzzy c-means algorithm produces good clusters