title: Hybrid hierarchical clustering with applications to microarray data.
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.
authors.affiliation: Department of Mathematics and Statistics, Acadia University, Wolfville, NS, Canada B4P 2R6. hugh.chipman@acadiau.ca
authors.forename: Hugh
authors.initials: H
authors.lastname: Chipman
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*