A Density-Based Algorithm for Discovering …

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From: KDD-96 Proceedings. Copyright 1996, AAAI ( ). All rights reserved. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databaseswith Noise Martin Ester, Hans-Peter Kriegel, Jiirg Sander, Xiaowei Xu Institute for ComputerScience, University of Munich Oettingenstr. 67, D-80538Miinchen, Germany {ester I kriegel I sander I xwxu} Abstract are often not knownin advancewhendealing with large Clusteringalgorithmsare attractive for the task of class iden- databases. tification in spatial , the applicationto (2) Discoveryof clusters with arbitrary shape, because the large spatial databasesrises the followingrequirements for shapeof clusters in spatial databases maybe spherical, clustering algorithms: minimalrequirements of domain drawn-out,linear, elongatedetc. knowledgeto determinethe input parameters,discoveryof clusters witharbitrary shapeandgoodefficiencyonlarge da- (3) Goodefficiency on large databases, on databases of tabases. Thewell-known clustering algorithmsoffer no solu- significantly morethan just a fewthousandobjects.

proach fails because there are two kinds of points in a clus-ter, points inside of the cluster (core points) and points on the border of the cluster (border points).

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