Parallel Algorithms for Hierarchical Clustering

Parallel Algorithms for Hierarchical Clustering (1995)

by Clark F. Olson

Venue:	Parallel Computing
Citations:	69 - 1 self

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Parallel Algorithms for Hierarchical Clustering	INFERENCE
AUTHOR NAME	Clark F. Olson	SVM HeaderParse 0.1
AUTHOR AFFIL	1; Computer Science Department; Cornell University	SVM HeaderParse 0.2
AUTHOR ADDR	Ithaca, NY 14853	SVM HeaderParse 0.1
ABSTRACT	Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. 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. Keywords. Hierarchical clustering, pattern analysis, parallel algorithm, butterfly network, PRAM algorithm. 1 In...	user correction - Legacy Corrections
YEAR	1995	INFERENCE
VENUE	Parallel Computing	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	1313--1325	INFERENCE
VOLUME	21	INFERENCE
CITATIONS	21 found	ParsCit 1.0