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Experiments in Parallel Clustering with DBSCAN

2001; Springer Science+Business Media; Linguagem: Inglês

10.1007/3-540-44681-8_46

1611-3349

Domenica Arlia, Massimo Coppola,

Advanced Clustering Algorithms Research

We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeletonstructured program that performs parallel exploration of each cluster. The approach is useful to improve performance on high-dimensional data, and is general w.r.t. the spatial index structure used. We report preliminary results of the application running on a Beowulf with good efficiency.