Optimal parallel merging and sorting without memory conflicts
IEEE Transactions on Computers
Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor
Journal of Documentation
Parallel Algorithms for Hierarchical Clustering and Cluster Validity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel algorithms for hierarchical clustering
Parallel Computing
Data mining: concepts and techniques
Data mining: concepts and techniques
Concurrent threads and optimal parallel minimum spanning trees algorithm
Journal of the ACM (JACM)
Optimal parallel algorithm for the knapsack problem without memory conflicts
Journal of Computer Science and Technology
Efficient Parallel Hierarchical Clustering Algorithms
IEEE Transactions on Parallel and Distributed Systems
pPOP: Fast yet accurate parallel hierarchical clustering using partitioning
Data & Knowledge Engineering
Fast parallel algorithm for distance transform
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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 1 ≤ p ≤ n/log n. Performance comparisons show that our algorithm is the first algorithm that is both without memory conflicts and adaptive.