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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.
(2) Discovery of clusters with arbitrary shape, because the shape of clusters in spatial databases may be spherical, drawn-out, linear, elongated etc. (3) Good efficiency on large databases, i.e. on databases of significantly more than just a few thousand objects. The well-known clustering algorithms offer no solution to
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