Hybrid hierarchical clustering with applications to microarray data.


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.

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@article{Chipman2006HybridHC, title={Hybrid hierarchical clustering with applications to microarray data.}, author={Hugh A. Chipman and Robert Tibshirani}, journal={Biostatistics}, year={2006}, volume={7 2}, pages={286-301} }