Abstract
A Parallel Constructing-Density-Tree Clustering Algorithm based on Data Partitioning (PCAP) was presented. The PCAP automatically partitioned global data space into load-balanced subspaces, which were distributed to different processors to complete subspaces’ clustering. The clustering result of global data space was achieved by merging those strong-association clusters though checking the association-intensity of leaves’ similarity. The detailed method of computing the association-intensity between clusters was described. Finally, the relevancy of the speedup and the amount of processors were discussed. The experiment results on artificial and real datasets show PCAP realizes the parallel of constructing-density-tree clustering algorithm and improves the clustering speed efficiently under preserving enough clustering precision. This approach is more suitable for dealing with great amounts of datasets.