We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.

A new approach of data clustering which is capable of detecting linked or crossed clusters, is proposed. In conventional clustering approaches, it is a hard work to separate linked or crossed clusters if the cluster prototypes are difficult to be represented by a mathematical formula. In this paper, we extract the force information from data points using the concept of psychological potential field, and utilize the information to measure the similarity between data points. Through several experiments, the force shows its effectiveness in diiscriminating different clusters even if they are linked or corssed.