Robust Distance-Based Clustering with Applications to Spatial Data Mining

作者: V. Estivill-Castro , M. E. Houle

DOI: 10.1007/S00453-001-0010-1

关键词: Data miningInformation extractionCluster (physics)Cluster analysisCombinatorial optimizationExploratory data analysisDelaunay triangulationData processingComputer scienceMedoid

摘要: In this paper we present a method for clustering geo-referenced data suitable applications in spatial mining, based on the medoid method. The is related to k -MEANS, with restriction that cluster representatives be chosen from among elements. Although general produces clusters of high quality, especially presence noise, it often criticized Ω(n 2 ) time requires. Our incorporates both proximity and density information achieve high-quality subquadratic time; does not require user specify number advance. bound achieved by means fast approximation objective function, using Delaunay triangulations store information.

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