APPLICATION OF PROBABILISTIC SETS TO FUZZY CLUSTERING.

Characterization of fuzzy clustering algorithms is dealt with using the concept of probabilistic sets and their subjective entropy. Clustering techniques with objective performance-index criterion-functions are discussed. Several indices characterized by subjective entropy, including indices expressing the degree of interaction between clusters, are proposed. The utility of these indices is illustrated by a simulation method using Iris data and also handwritten-character data.