Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

6-2009

Journal or Book Title

Biometrics

Volume

65

Issue

2

First Page

341

Last Page

352

DOI

10.1111/j.1541-0420.2008.01064.x

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.

Comments

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.

Copyright Owner

The International Biometric Society

Language

en

File Format

application/pdf

Published Version

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