Fuzzy k-means clustering with crisp regions

A new fuzzy k-means clustering method is proposed by introducing crisp regions of clusters. Boundaries of the regions are determined by hyperbolas and membership values are given by one or zero in each region. The area between crisp regions is a fuzzy region, where membership values are proportional to distances to crisp regions. A new method is a direct extension of the traditional hard k-means