Parallel Algorithms for Hierarchical Clustering and Cluster Validity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel database systems: the future of high performance database systems
Communications of the ACM
Scalable parallel geometric algorithms for coarse grained multicomputers
SCG '93 Proceedings of the ninth annual symposium on Computational geometry
Parallel algorithms for hierarchical clustering
Parallel Computing
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
SIMD Algorithms for Single Link and Complete Link Pattern Clustering
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Fast parallel algorithm for the single link heuristics of hierarchical clustering
SPDP '92 Proceedings of the 1992 Fourth IEEE Symposium on Parallel and Distributed Processing
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Hierarchical clustering methods are important in many data mining and pattern recognition tasks. In this paper we present an efficient coarse grained parallel algorithm for Single Link Clustering; a standard inter-cluster linkage metric. Our approach is to first describe algorithms for the Prefix Larger Integer Set and the Closest Larger Ancestor problems and then to show how these can be applied to solve the Single Link Clustering problem. In an extensive performance analysis an implementation of these algorithms on a Linux-based cluster has shown to scale well, exhibiting near linear relative speedup.