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

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References in zbMATH (referenced in 4 articles , 1 standard article )

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  1. Mai, Son T.; Assent, Ira; Jacobsen, Jon; Dieu, Martin Storgaard: Anytime parallel density-based clustering (2018)
  2. Yıldırım, Ahmet Artu; Özdoğan, Cem: Parallel WaveCluster: A linear scaling parallel clustering algorithm implementation with application to very large datasets (2011) ioport
  3. Kulldorff, Martin: Tests of spatial randomness adjusted for an inhomogeneity: a general framework (2006)
  4. Arlia, Domenica; Coppola, Massimo: Experiments in parallel clustering with DBSCAN (2001)