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Parallel Algorithms for Hierarchical Clustering
 Parallel Computing
, 1995
"... 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 f ..."
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Cited by 104 (2 self)
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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...
Parallelism in knowledge discovery techniques
 LNCS 2367: Applied Parallel Computing, 6th International Conference PARA’02
, 2002
"... Abstract. Knowledge discovery in databases or data mining is the semiautomated analysis of large volumes of data, looking for the relationships and knowledge that are implicit in large volumes of data and are ’interesting’ in the sense of impacting an organization’s practice. Data mining and knowled ..."
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Cited by 2 (0 self)
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Abstract. Knowledge discovery in databases or data mining is the semiautomated analysis of large volumes of data, looking for the relationships and knowledge that are implicit in large volumes of data and are ’interesting’ in the sense of impacting an organization’s practice. Data mining and knowledge discovery on large amounts of data can benefit of the use of parallel computers both to improve performance and quality of data selection. This paper presents and discusses different forms of parallelism that can be exploited in data mining techniques and algorithms. For the main data mining techniques, such as rule induction, clustering algorithms, decision trees, genetic algorithms, and neural networks, the possible ways to exploit parallelism are presented and discussed in detail. Finally, some promising research directions in the parallel data mining research area are outlined. 1
PARALLEL COMPUTING Parallel algorithms for hierarchical clustering
, 1993
"... Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n*) 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 hier ..."
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Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n*) 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 PRAh4, butterfly, or tree.