Data mining: concepts and techniques
Data mining: concepts and techniques
On Improving Clustering in Numerical Databases with Artificial Ants
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
An Adaptive Flocking Algorithm for Spatial Clustering
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
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Clustering has become an increasingly important task in modern application domains such as electronic commerce, multimedia, surveillance using sensor networks as well as many others. In many of these areas, the data are originally collected at different sites and their transmission to a central site is almost impossible. This requires to develop novel distributed clustering algorithms to handle the difficult problems posed from the dynamic topology changes of the network, impracticality of global communications and global synchronization and the frequent failure and recovery of resources. 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 local flocking algorithm that uses a decentralized approach to discover clusters by a density-based 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.