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A Density-Based Algorithm for Discovering Clusters in ...

A Density-Based Algorithm for Discovering Clustersin Large Spatial Databases with NoiseMartin Ester, Hans-Peter Kriegel, Jiirg Sander, Xiaowei XuInstitute for Computer Science, University of MunichOettingenstr. 67, D-80538 Miinchen, Germany{ester I kriegel I sander I xwxu } algorithms are attractive for the task of class iden-tification in spatial databases. However, the application tolarge spatial databases rises the following requirements forclustering algorithms: minimal requirements of domainknowledge to determine the input parameters, discovery ofclusters with arbitrary shape and good efficiency on large da-tabases. The well-known clustering algorithms offer no solu-tion to the combination of these requirements. In this paper,we present the new clustering Algorithm DBSCAN relying ona Density-Based notion of Clusters which is designed to dis-cover Clusters of arbitrary shape.

ters, Efficiency on Large Spatial Databases, Handling Nlj4-275oise. 1. Introduction Numerous applications require the management of spatial data, i.e. data related to space. Spatial Database Systems (SDBS) (Gueting 1994) are database systems for the man-agement of spatial data. Increasingly large amounts of data

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A Density-Based Algorithm for Discovering …

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A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise Martin Ester, Hans-Peter Kriegel, Jiirg Sander, Xiaowei Xu

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