Research on Vague soft clustering algorithm based on MapReduce

Aiming at the problems that traditional clustering algorithm based on Vague soft sets is difficult to deal with massive data, a parallel Vague soft clustering algorithm based on MapReduce is proposed. The algorithm calculate the similarity measure between Vague soft sets based on Map function and Reduce function of the MapReduce programming framework, and Vague similarity matrix is established as the basis for clustering at the same time. Secondly, the Vague matrix is partitioned according to the similarity matrix based on the idea of matrix partition, and we process the block matrix and merge the results based on MapReduce. Finally, the data items is clustered based on the similarity threshold. The contrast experiment between traditional Vague soft clustering algorithm and the new algorithm shows that the proposed algorithm has higher accuracy, it can not only achieve better speedup in large-scale data calculation but also divide project resources effectively and accurately.