Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor
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A fast distributed algorithm for mining association rules
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Halo World: Tools for Parallel Cluster Finding inAstrophysical N-body Simulations
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Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
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InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
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BNCOD'07 Proceedings of the 24th British national conference on Databases
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Information Sciences: an International Journal
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ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure
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Frontiers of Computer Science: Selected Publications from Chinese Universities
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The clustering algorithm DBSCAN relies on a density-basednotion of clusters and is designed to discover clusters ofarbitrary shape as well as to distinguish noise. In this paper, wepresent PDBSCAN, a parallel version of this algorithm. We use the ‘shared-nothing’ architecture with multiple computers interconnectedthrough a network. A fundamental component of a shared-nothing systemis its distributed data structure. We introduce the dR*-tree, a distributed spatial index structure in which the data is spread amongmultiple computers and the indexes of the data are replicated onevery computer. We implemented our method using a number ofworkstations connected via Ethernet (10 Mbit). A performanceevaluation shows that PDBSCAN offers nearly linear speedup and hasexcellent scaleup and sizeup behavior.