A uniformity criterion and algorithm for data clustering

Sanketh Shetty, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a novel multivariate uniformity criterion for testing uniformity of point density in an arbitrary dimensional point pattern . An unsupervised, nonparametric data clustering algorithm, using this criterion, is also presented. The algorithm relies on a relatively general notion of cluster so that it is applicable to clusters of relatively unrestricted shapes, densities and sizes. We define a cluster as a set of contiguous interior points surrounded by border points. We use our uniformity test to differentiate between interior and border points. We group interior points to form cluster cores, and then identify cluster borders as formed by the border points neighboring the cluster cores. The algorithm is effective in resolving clusters of different shapes, sizes and densities. It is relatively insensitive to outliers. We present results for experiments performed on artificial and real data sets.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
StatePublished - Dec 1 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: Dec 8 2008Dec 11 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2008 19th International Conference on Pattern Recognition, ICPR 2008
CountryUnited States
CityTampa, FL
Period12/8/0812/11/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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    Shetty, S., & Ahuja, N. (2008). A uniformity criterion and algorithm for data clustering. In 2008 19th International Conference on Pattern Recognition, ICPR 2008 [4761239] (Proceedings - International Conference on Pattern Recognition).