An Efficient Distributed Data Clustering Algorithm

The k-means algorithm is one of the most popular clustering algorithms in use today. The high running time complexity of serial k-means limits its applicability for very large databases. On the other hand, the existing parallel kmeans algorithms demand huge data transfer operations incorporating high communication complexity. Transfer of actual data from local sites is also unacceptable, in many situations, on security and privacy grounds. This work proposes a distributed clustering algorithm that can be executed in a distributed network of processors to achieve significant reduction in computation and communication times while identifying the cluster structure inherent to a data set. The large amount of data transfer, involved in parallel k-means algorithms, is avoided to reduce the communication overhead as well as to ensure better security and privacy. Results of experimentation show that the proposed distributed approach can provide higher speedup than other reported algorithms and can effectively be employed in large applications.

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