Clustering has happened to an increasingly important task in analyzing huge amounts of data. Traditional applications need that all data has to be located at the site where it is scrutinized. Nowadays, large amounts of heterogeneous, difficult records reside on different, independently working computers which are connected to each other via local or large area networks. Preliminary work on an algorithm for K-means clustering of homogeneously distributed data in a peer-to-peer system. The algorithm is asynchronous and every node operates locally by communicating only with its topological neighboring nodes. due to network bandwidth restrictions, or sense of the huge amount of distributed data. Due to the theatrical enlarge of data volumes in a different Appliance, it is suitably infeasible to keep these data in one centralized machine. It is suitable more and more natural to deal with distributed databases and networks. That is why distributed data mining technique includes. Individual of the mainly important data mining problems is data clustering. We regard as the clustering of very huge datasets distributed over a network of computational units using a decentralized K-means algorithm. To attain the equal codebook at every node of the network, we use a randomized gossip aggregation protocol where only small messages are exchanged.

Distributed systems, clustering, partition-based clustering, density-based clustering, dynamic system

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