Clustering in the Presence of Scatter

Date
2009-06-01
Authors
Maitra, Ranjan
Ramler, Ivan
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Abstract

A new methodology is proposed for clustering datasets in the presence of scattered observations. Scattered observations are defined as unlike any other, so traditional approaches that force them into groups can lead to erroneous conclusions. Our suggested approach is a scheme which, under assumption of homogeneous spherical clusters, iteratively builds cores around their centers and groups points within each core while identifying points outside as scatter. In the absence of scatter, the algorithm reduces to k-means. We also provide methodology to initialize the algorithm and to estimate the number of clusters in the dataset. Results in experimental situations show excellent performance, especially when clusters are elliptically symmetric. The methodology is applied to the analysis of the United States Environmental Protection Agency’s Toxic Release Inventory reports on industrial releases of mercury for the year 2000.

Description

This is the peer reviewed version of the following article: Maitra,R., and Ramler, I. Clustering in the presence of scatter. Biometrics, 65: 341-352, which has been published in final form at http://dx.doi.org/10.1111/j.1541-0420.2008.01064.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.

Keywords
Bayes Information Criterion, biweight estimator, exact-c-separation, k-clips, MCLUST, methylmercury, tight clustering
Citation
DOI
Collections