1998 DensityBasedClusteringinSpatial

• (Sander et al., 1998) ⇒ Jörg Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. (1998). “Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications.” In: Data Mining and Knowledge Discovery, 2(2). doi:10.1023/A:1009745219419

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Density-Based+Clustering+in+Spatial+Databases%3A+The+Algorithm+GDBSCAN+and+Its+Applications%22+1998
• http://dl.acm.org/citation.cfm?doid=A:1009745219419&preflayout=flat#citedby

Quotes

Abstract

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 generalize this algorithm in two important directions. The generalized algorithm — called GDBSCAN — can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. In addition, four applications using 2D points (astronomy), 3D points (biology), 5D points (earth science) and 2D polygons (geography) are presented, demonstrating the applicability of GDBSCAN to real-world problems.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 DensityBasedClusteringinSpatial	Martin Ester Hans-Peter Kriegel Xiaowei Xu Jörg Sander			Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications			http://www.dss.dpem.tuc.gr/pdf/GDBSCAN.pdf	10.1023/A:1009745219419