Transcription of A Density-Based Algorithm for Discovering …
{{id}} {{{paragraph}}}
Example: dental hygienist
A Density-Based Algorithm for Discovering …
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-80538 Miinchen, 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).
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: