Fuzzy clustering with Gaussian-type member ship function

A Characteristic of the crisp clustering technique is that the boundary between clusters is fully defined. However, in many real-time situations, the boundaries between clusters cannot be clearly identified. Some patterns may belong to more than one cluster. In such cases, the fuzzy clustering method provides a better and more useful method to classify these patterns. Fuzzy c-means (FCM) FCM method is applicable to a wide variety of geostatistical data-analysis problems. This method generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructures in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. We have implemented FCM algorithm with Gaussian membership values. Features of this method include a choice of an adjustable weighting factor that essentially controls sensitivity to the number of clusters.