SIMD algorithms for single link and complete link pattern clustering

Abstract

In this paper, new parallel algorithms for single and complete link hierarchical agglomerative clustering are presented. The parallel algorithms have been mapped on a SIMD machine model with a linear interconnection network. The model consists of a linear array of N PE's, where N is the number of patterns, interfaced with a global host machine and the interconnection network provides inter-PE and PE-to-host/host-to-PE communication. The proposed algorithms are faster than previously known algorithms for hierarchical clustering. For clustering a data set with N patterns, using N PE's, the computation time for the single link clustering algorithm is shown to be O(NlogN) and that for the complete link clustering algorithm is shown to be O(N/sup 2/). The parallel algorithms have been verified through simulations on the Intel's iPSC/2 concurrent supercomputer.