Towards interval-valued fuzzy set-based collaborative fuzzy clustering algorithms.

Several studies were devoted to the usage of the Fuzzy C-Means (FCM) algorithm to collaborative clustering, especially in the realm of data analysis, data mining, and pattern recognition. In this study, a novel interval-valued fuzzy set-based approach to realize collaborative clustering is presented. In collaborative clustering diagram, the local clustering results acquired locally (at a specific data site) impact clustering carried out at some other data sites. Those clustering methods endowed with interval-valued fuzzy sets help cope with uncertainties present in the data and the nature of the collaborative process itself. The validity indices such as fuzzy silhouette and SSE (Sum of Squared Error) are extended to quantify results produced by collaborative fuzzy clustering. Several experimental studies are presented using which we demonstrate the advantages of the proposed algorithms. [ABSTRACT FROM AUTHOR]