STUDY OF HIERARCHICAL CLUSTERING PARALLEL COMPUTATION ON PRAM MODEL

Abstract

An adaptive parallel algorithm for hierarchical clustering based on PRAM model was presented. The following approaches were devised to produce the optimized clustered data set, including the data preprocessing based on "90-10" rule to decrease the size of the data set, progressively the parallel algorithm to create Euclid minimum spanning trees on absolute graph, and the algorithm that determined the split strategies and dealt with the memory conflicts. The data set was clustered based on the noncollision memory, the lowest cost, and weakest PRAM-EREW model. N data sets were clustered in O((λn)2/p) time (0.1 ≤ λ ≤ 0.3) by performing this algorithm using p processors (1 ≤ p ≤ n/ log (n)). The parallel hierarchical clustering algorithm based on PRAM model was adaptive, and of noncollision memory. The computing time could be significantly reduced after original inputting data was effectually preprocessed through the improved preprocessing methods presented in this paper.