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

Fallah Shafagh, Tritchler David and Beyene Joseph
Additional contact information
Fallah Shafagh: University of Toronto
Tritchler David: University Health Network, Toronto; University of Toronto; and SUNY at Buffalo
Beyene Joseph: Hospital for Sick Children Research Institute and University of Toronto

Statistical Applications in Genetics and Molecular Biology, 2008, vol. 7, issue 1, 25

Abstract: 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.

Keywords: cluster analysis; eigenanalysis; microarray; segmented regression; scree plot (search for similar items in EconPapers)
Date: 2008
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DOI: 10.2202/1544-6115.1261

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