Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data

Many clustering methods require that the number of clusters believed present in a given data set be specified a priori, and a number of methods for estimating the number of clusters have been developed. However, the selection of the number of clusters is well recognized as a difficult and open problem and there is a need for methods which can shed light on specific aspects of the data. This paper adopts a model for clustering based on a specific structure for a similarity matrix. Publicly available gene expression data sets are analyzed to illustrate the method and the performance of our method is assessed by simulation.