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Biostatistics Advance Access published online on November 21, 2005
Biostatistics, doi:10.1093/biostatistics/kxj007
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.
Received July 28, 2003
Revised August 30, 2005
Accepted November 3, 2005

Article

Hybrid Hierarchical Clustering with Applications to Microarray Data

Hugh Chipman 1 * and Robert Tibshirani 2

1 Hugh Chipman is Associate Professor and Canada Research Chair in Mathematical Modelling, Department of Mathematics and Statistics, Acadia University
2 Robert Tibshirani is Professor of Health Research and Policy, and Statistics, Stanford University

* To whom correspondence should be addressed.
Hugh Chipman, E-mail: hugh.chipman{at}acadiau.ca


  Abstract

In this paper we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other points. Theoretical connections between mutual clusters and bottom-up clustering methods are established, aiding in their interpretation and providing an algorithm for identification of mutual clusters. We illustrate the technique on simulated and real microarray datasets.

Keywords: top-down clustering; bottom-up clustering; mutual cluster.
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