Lightweight clustering technique for distributed data mining applications
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
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In this paper, we proposed a new clustering algorithm that employs the concept of message passing to describe parallel and spontaneous biological processes. Inspired by real-life situations in which people in large gatherings form groups by exchanging messages, Message Passing Clustering (MPC) allows data objects to communicate with each other and produces clusters in parallel, thereby making the clustering process intrinsic and improving the clustering performance. We have proved that MPC shares similarity with hierarchical clustering but offers significantly improved performance because it takes into account both local and global structure. MPC can be easily implemented in a parallel computing platform for the purpose of speed-up. To validate the MPC method, we applied MPC to microarray data from the Stanford yeast cell-cycle database. The results show that MPC gave better clustering solutions in terms of homogeneity and separation values than other clustering methods.