Parallel algorithms for hierarchical clustering

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 [3,4,11,19]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hi...

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Veröffentlicht in:Parallel computing Jg. 21; H. 8; S. 1313 - 1325
1. Verfasser: Olson, Clark F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.08.1995
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Butterfly network
Computer science; control theory; systems
Exact sciences and technology
Hierarchical clustering
Parallel algorithm
Pattern analysis
PRAM algorithm
Theoretical computing
Butterfly network
Parallel algorithm
Hierarchical clustering
Pattern analysis
PRAM algorithm
Aggregation
Sequential
Optimal algorithm
Multidimensional space
Metric
Optimization
ISSN:0167-8191, 1872-7336
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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 [3,4,11,19]. 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.
ISSN:0167-8191
1872-7336
DOI:10.1016/0167-8191(95)00017-I