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Biostatistics Advance Access originally published online on November 21, 2005
Biostatistics 2006 7(2):286-301; 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.

Hybrid hierarchical clustering with applications to microarray data

Hugh Chipman*

Department of Mathematics and Statistics, Acadia University, Wolfville, NS, Canada B4P 2R6 hugh.chipman{at}acadiau.ca

Robert Tibshirani

Department of Health Research and Policy and Department of Statistics, Stanford University, Stanford, CA 94305 tibs{at}stanford.edu

* To whom correspondence should be addressed.

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: Bottom-up clustering; Mutual cluster; Top-down clustering


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