Distributed Anytime Clustering Using Biologically Inspired Systems

In this paper, we propose a biologically-inspired algorithm for clustering distributed data in a peer-to-peer network with a small world topology. The method proposed is based on a set of locally executable flocking algorithms that use a decentralized approach to discover clusters by an adaptive nearest-neighbor non-hierarchical approach and the execution, among the peers, of an iterative self-labeling strategy to generate global labels with which identify the clusters of all peers. We have measured the goodness of our flocking search strategy on performance in terms of accuracy and scalability. Furthermore, we evaluated the impact of small world topology in terms of reduction of iterations and messages exchanged to merge clusters.