Parallel algorithms for hierarchical clustering

This paper discusses parallel algorithms to perform hierarchical clustering using various distance metrics, and a general algorithm is given that can be used to perform clustering with the complete link and average link metrics on a butterfly.

Abstract

Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n2) algorithms are known for this problem [3,4,11,19]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n/log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n/log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n/log n processor PRAM, butterfly, or tree.