This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

Density‐based clustering

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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

Density‐distributions of data points and density‐based clusters for different density levels. Different colors indicate different clusters or noise.

[ Normal View | Magnified View ]

Reachability plot for a sample 2D data set.

[ Normal View | Magnified View ]

The impact of different kernels on the density estimation.

[ Normal View | Magnified View ]

Density connectivity.

[ Normal View | Magnified View ]

Iris data (green: I. setosa, red: I. versicolor, blue: I. virginica).

[ Normal View | Magnified View ]

Maunga Whau Volcano (Mt Eden).

[ Normal View | Magnified View ]

Browse by Topic

Technologies > Structure Discovery and Clustering

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts