| dc.description.abstract | Recently, efficiency of clustering algorithms for large data analysis has become a major issue in many application domains. First, the computation time and storage space increases dramatically while processing a large amount of data. Second, it is hard to determine the number of clusters automatically. In order to cluster datasets within rational computation time and storage space, we propose a parallel computing strategy using the concept of graph-based clustering and granular computing. The proposed strategy automatically determines the best number of clusters of a large datasets, and effectively reduces the computation time and storage space requirements, given a large amount of data. Based on the proposed strategy, we devise two clustering algorithms, and implement them in Message Passing Interface (MPI). Both the algorithms utilize the minimum spanning tree structure to determine the best number of clusters automatically. The first algorithm is called Para-CPLM (Parallel Clustering based on Partitions of Local Minimal-spanning-trees), while the second algorithm is called Para-CGM (Parallel Clustering based on a Global Minimal-spanning-tree).
The Para-CPLM partitions the data domain into several blocks (hyper-rectangles) according to the dimensions of the datasets, utilizes a parallel method to uniformly distribute all datasets to the blocks, and then establishes a local minimal-spanning-tree in each block. After each local minimal-spanning-tree is established, it combines those local minimal-spanning-trees according to the closest Euclidean Distance and then applies the GBC method to cluster the local minimal-spanning trees. After the first clustering, it checks the distances between each cluster, and finds the closest Euclidean Distance that conforms to the rules of the minimal-spanning-tree among them. The Para-CGM constructs a global minimal-spanning-tree in parallel, and applies the GBC method to find the best number of clusters. It requires a given threshold number to process the data efficiently.
From our experimental results, the Para-CPLM has significantly shorter execution time and almost the same clustering results when compared with the GBC method. On the contrary, the Para-CGM has less improvement on execution time than the Para-CPLM, given that there are enough computing resources for the Para-CPLM. However, it still outperforms the GBC method and produces the same clustering results.
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