Parallel Algorithms for Image Template Matching on Hypercube SIMD Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
A probabilistic measure of similarity for binary data in pattern recognition
Pattern Recognition
Computer Architecture and Parallel Processing
Computer Architecture and Parallel Processing
Comments on 'Parallel Algorithms for Hierarchical Clustering and Cluster Validity'
IEEE Transactions on Pattern Analysis and Machine Intelligence
On storage schemes for parallel array access
ICS '92 Proceedings of the 6th international conference on Supercomputing
A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering
Journal of Global Optimization
Optimal algorithms for complete linkage clustering in d dimensions
Theoretical Computer Science
Efficient Parallel Hierarchical Clustering Algorithms
IEEE Transactions on Parallel and Distributed Systems
Hierarchical clustering of gene expression profiles with graphics hardware acceleration
Pattern Recognition Letters
Faster and more robust point symmetry-based K-means algorithm
Pattern Recognition
pPOP: Fast yet accurate parallel hierarchical clustering using partitioning
Data & Knowledge Engineering
Exploiting parallelism to support scalable hierarchical clustering
Journal of the American Society for Information Science and Technology
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
An adaptive parallel hierarchical clustering algorithm
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Behavior scoring model for coalition loyalty programs by using summary variables of transaction data
Expert Systems with Applications: An International Journal
Future Generation Computer Systems
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Parallel algorithms on SIMD (single-instruction stream multiple-data stream) machines for hierarchical clustering and cluster validity computation are proposed. The machine model uses a parallel memory system and an alignment network to facilitate parallel access to both pattern matrix and proximity matrix. For a problem with N patterns, the number of memory accesses is reduced from O(N/sup 3/) on a sequential machine to O(N/sup 2/) on an SIMD machine with N PEs.