A New Proposal for a Granular Fuzzy C-Means Algorithm

Abstract

Fuzzy clustering algorithms are able to find the centroids and partition matrices, but are predominantly numerical, although each cluster prototype can be considered as a granule of information it continues to be a numeric value, in order to give a similar representation structure data. Granular theory and clustering algorithms can be combined to achieve this goal, resulting in granular prototypes and granular matrices of belonging and a more reflective data structure.