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2007
Springer

An Adaptive Parallel Hierarchical Clustering Algorithm

9 years 2 months ago
An Adaptive Parallel Hierarchical Clustering Algorithm
Clustering of data has numerous applications and has been studied extensively. It is very important in Bioinformatics and data mining. Though many parallel algorithms have been designed, most of algorithms use the CRCW-PRAM or CREW-PRAM models of computing. This paper proposed a parallel EREW deterministic algorithm for hierarchical clustering. Based on algorithms of complete graph and Euclidean minimum spanning tree, the proposed algorithms can cluster n objects with O(p) processors in O(n2 /p) time where n n p log 1 ≤≤ . Performance comparisons show that our algorithm is the first algorithm that is both without memory conflicts and adaptive.
Zhaopeng Li, Kenli Li, Degui Xiao, Lei Yang
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where HPCC
Authors Zhaopeng Li, Kenli Li, Degui Xiao, Lei Yang
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