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Parallel Implementation of Hybrid Clustering

There are three kinds of clustering approaches: partitioning clustering, hierarchical clustering, and hybrid clustering in terms of cluster structures. Hierarchical clustering is more flexible than partitioning clustering but very expensive for large data sets. Hybrid clustering combines the features of hierarchical and partitioning clustering. In this paper, we propose a parallel hybrid clustering method which is implemented using MPI (message passing interface). We run (ran) the parallel program on different numbers of computer nodes/machines and compared the results with the sequential program. Experiments show that our parallel approach is more efficient than the sequential clustering and produces better results when the number of machines is large.