Experiments in parallel clustering with DBSCAN. 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 skeleton-structured 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
References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Yıldırım, Ahmet Artu; Özdoğan, Cem: Parallel WaveCluster: A linear scaling parallel clustering algorithm implementation with application to very large datasets (2011)
- Kulldorff, Martin: Tests of spatial randomness adjusted for an inhomogeneity: a general framework (2006)
- Arlia, Domenica; Coppola, Massimo: Experiments in parallel clustering with DBSCAN (2001)