title: Rough-fuzzy collaborative clustering.
abstract: In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata. sushmita@isical.ac.in
authors.forename: Sushmita
authors.initials: S
authors.lastname: Mitra
authors.forename: Haider
authors.initials: H
authors.lastname: Banka
authors.forename: Witold
authors.initials: W
authors.lastname: Pedrycz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003299:Cooperative Behavior; D017143:Fuzzy Logic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*