Using Gaussians Functions to Determine Representative Clustering Prototypes

Clustering is a process for grouping a set of objects into classes or clusters so that the objects within a cluster have high similarity, but are very dissimilar to objects in other clusters. Choosing cluster centers is crucial during clustering process. In this paper, we propose an improved fuzzy clustering approach, named FGWC (fuzzy Gaussian weights clustering). We compared FGWC with an enhanced fuzzy C-means (EFCM) clustering approach that we already presented. The EFCM determines automatically the number of clusters which is a user-defined parameter for FCM, and uses the fuzzy weights to compute cluster prototypes, but does nor take into account the distribution of the clusters. FGWC uses Gaussian functions for determining clustering prototypes. The generated cluster centers are more representative and accurate with FGWC than with EFCM