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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.

able for applications such as character recognition with moderate values for n, but it is prohibitive for applications on large databases. Jain (1988) explores a density based approach to identify clusters in k-dimensional point sets. The data set is parti-tioned into a number of nonoverlapping cells and histograms are constructed.

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