A granular recursive fuzzy meta-clustering algorithm for social networks

This paper uses the concepts of fuzzy membership and granularity proposed by Zadeh to propose a fuzzy meta-clustering algorithm for creating associated profiles of networked granules. The proposed algorithm uses repeated applications of fuzzy c-means algorithm to create soft clustering. Representation of a granule is recursively updated using the fuzzy cluster memberships of other connected granules. These fuzzy memberships are obtained from the previous application of clustering. The fuzzy cluster memberships enhance the traditional representation of a granule derived from the primary source of data by recording events such as transactions, phone calls, user sessions, security breaches, and car trips. The proposed approach extends a previous recursive meta-clustering algorithm based on crisp k-means clustering. The use of fuzzy memberships is shown to create more meaningful recursive profiles of a social network of phone users.