A Fast Parallel Clustering Algorithm for Large Spatial Databases

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the `shared-nothing' architecture with multiple computers interconnected through a network. A fundamental component of a shared-nothing system is its distributed data structure. We introduce the dR-tree, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer. We implemented our method using a number of workstations connected via Ethernet (10 Mbit). A performance evaluation shows that PDBSCAN offers nearly linear speedup and has excellent scaleup and sizeup behavior.

• Type: Journal
• Year: 1999
• Where: DATAMINE
• Authors: Xiaowei Xu, Jochen Jäger, Hans-Peter Kriegel
• Keywords: Clustering Algorithm Dbscan, DATAMINE 1999, Distributed Spatial Index, Multiple Computers