2003
DOI: 10.1007/3-540-36175-8_56
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DBRS: A Density-Based Spatial Clustering Method with Random Sampling

Abstract: When analyzing spatial databases or other datasets with spatial attributes, one frequently wants to cluster the data according to spatial attributes. In this paper, we describe a novel density-based spatial clustering method called DBRS. The algorithm can identify clusters of widely varying shapes, clusters of varying densities, clusters which depend on non-spatial attributes, and approximate clusters in very large databases. DBRS achieves these results by repeatedly picking an unclassified point at random and… Show more

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Cited by 67 publications
(35 citation statements)
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“…Each record includes x and y coordinators and one nonspatial property. For all experiments described, Eps is 4, MinPts is 10, and MinPur is set to 0.75; extensive previous experiments with DBRS showed that these values give representative behaviors for the algorithm (Wang and Hamilton 2003). Each numeric value represents the average value from 10 runs.…”
Section: Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…Each record includes x and y coordinators and one nonspatial property. For all experiments described, Eps is 4, MinPts is 10, and MinPur is set to 0.75; extensive previous experiments with DBRS showed that these values give representative behaviors for the algorithm (Wang and Hamilton 2003). Each numeric value represents the average value from 10 runs.…”
Section: Resultsmentioning
confidence: 99%
“…DBRS is a density-based clustering method with three parameters, Eps, MinPts and MinPur (Wang and Hamilton 2003). DBRS repeatedly picks an unclassified point at random and examines its neighborhood, i.e., all points within a radius Eps of the chosen point.…”
Section: Density-based Spatial Clustering With Random Sampling (Dbrs)mentioning
confidence: 99%
See 2 more Smart Citations
“…The concept of visible space is introduced into DBCluC [3] which is based on DBSCAN [4]. DBRS+ [5] extending the density-based clustering method DBRS [6] can handle any combination of intersecting obstacles. AUTOCLUST+ [7] using Delaunay diagram not only detects clusters of arbitrary shapes, but also clusters of different densities.…”
Section: Related Workmentioning
confidence: 99%