A collaborative fuzzy clustering algorithm in distributed network environments

Due to privacy and security requirements or technical constraints, traditional centralized approaches to data clustering in a large dynamic distributed peer-to-peer network are difficult to perform. In this paper, a novel collaborative fuzzy clustering algorithm is proposed, in which the centralized clustering solution is approximated by performing distributed clustering at each peer with the collaboration of other peers. The required communication links are established at the level of cluster prototype and attribute weight. The information exchange only exists between topological neighboring peers. The attribute-weight-entropy regularization technique is applied in the distributed clustering method to achieve an ideal distribution of attribute weights, which ensures good clustering results. And the important features are successfully extracted for the high-dimensional data clustering. The kernelization of the proposed algorithm is also realized as a practical tool for clustering the data with "nonspherical"-shaped clusters. Experiments on synthetic and real-world datasets have demonstrated the efficiency and superiority of the proposed algorithms. © 2013 IEEE.