A relational fuzzy c-means clustering algorithm based on multiple dissimilarity matrices

This paper introduces a relational fuzzy c-means clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each algorithm´s iteration and are different from one cluster to another. Experiments with datasets from UCI machine learning repository show the usefulness of the proposed algorithm.