Erich P. Stuntebeck, John S. Davis II, et al.
HotMobile 2008
According to the efficiency bottleneck of algorithm DBSCAN, we present P-DBSCAN, a novel parallel version of this algorithm in distributed environment. By separating the database into several parts, the computer nodes carry out clustering independently; after that, the sub-results will be aggregated into one final result. P-DBSCAN achieves good results and much better efficiency than DBSCAN. Experiments show that, P-DBSCAN accelerates more than 40% on one PC, and 60% on two PCs. In addition, the parallel algorithm has much better scalability than DBSCAN, so that it can be used for clustering analysis in huge databases. © 2010 IEEE.
Erich P. Stuntebeck, John S. Davis II, et al.
HotMobile 2008
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ITA Conference 2007
Pradip Bose
VTS 1998
Ehud Altman, Kenneth R. Brown, et al.
PRX Quantum