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.