Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

1-30-2012

Journal or Book Title

Journal of the American Statistical Association

Volume

107

Issue

497

First Page

378

Last Page

392

DOI

10.1080/01621459.2011.646935

Abstract

This article proposes a bootstrap approach for assessing significance in the clustering of multidimensional datasets. The procedure compares two models and declares the more complicated model a better candidate if there is significant evidence in its favor. The performance of the procedure is illustrated on two well-known classification datasets and comprehensively evaluated in terms of its ability to estimate the number of components via extensive simulation studies, with excellent results. The methodology is also applied to the problem of k-means color quantization of several standard images in the literature and is demonstrated to be a viable approach for determining the minimal and optimal numbers of colors needed to display an image without significant loss in resolution. Additional illustrations and performance evaluations are provided in the online supplementary material.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on January 30, 2012, available online: http://www.tandf.com/10.1080/01621459.2011.646935.

Copyright Owner

Taylor & Francis

Language

en

File Format

application/pdf

Published Version

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