Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2010

Journal or Book Title

Journal of Computational and Graphical Statistics

Volume

19

Issue

2

First Page

377

Last Page

396

DOI

10.1198/jcgs.2009.08155

Abstract

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized observations such as those that occur when using Euclidean distance to cluster time series gene expression data using a correlation metric. We also provide methodology to initialize the algorithm and to estimate the number of clusters in the dataset. Results from a detailed series of experiments show excellent performance, even with very large datasets. The methodology is applied to the analysis of the mitotic cell division cycle of budding yeast dataset of Cho et al. [Molecular Cell (1998), 2, 65–73]. The entire dataset has not been analyzed previously, so our analysis provides an understanding for the complete set of genes acting in concert and differentially. We also use our methodology on the submitted abstracts of oral presentations made at the 2008 Joint Statistical Meetings (JSM) to identify similar topics. Our identified groups are both interpretable and distinct and the methodology provides a possible automated tool for efficient parallel scheduling of presentations at professional meetings.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphicla Statistics in 2010, available online: http://www.tandf.com/10.1198/jcgs.2009.08155.

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf

Published Version

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