Density‐based clustering

Hans‐Peter Kriegel(Ludwig-Maximilians-Universität München), Peer Kröger(Ludwig-Maximilians-Universität München), Jörg Sander(University of Alberta), Arthur Zimek(Ludwig-Maximilians-Universität München)
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
April 5, 2011
Cited by 807

Abstract

Abstract Clustering refers to the task of identifying groups or clusters in a data set. In density‐based clustering , a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density‐based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low‐density regions are typically considered noise or outliers. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 231–240 DOI: 10.1002/widm.30 This article is categorized under: Technologies > Structure Discovery and Clustering


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