Parallelization study on the clustering technique to mine large datasets

Parallel clustering algorithm implementations concerning message passing interface (MPI) and compute unified device architecture (CUDA) model with their applications to very large datasets have been presented in the thesis. WaveCluster is a novel clustering approach based on wavelet transforms. Despite it?s novelty, it requires considerable amount of time to collect results for large sizes of multidimensional datasets. In the MPI algorithm; divide and conquer approach has been followed and communication among processors are kept at minimum to achieve high efficiency. Developed parallel WaveCluster algorithm exposes high speedup and scales linearly with the increasing number of processors. Parallel behavior of WaveCluster approach has been also investigated by executing the algorithm on graphical processing unit (GPU). High speedup values have been obtained in the computation of wavelet transform and connected component labeling algorithms in the GPUs with respect to the sequential algorithms running on the CPU.