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.
Editor-in-Chief: Stumpf, Michael P.H.
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Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data
Shafagh Fallah / David Tritchler / Joseph Beyene
1University of Toronto
1University Health Network, Toronto; University of Toronto; and SUNY at Buffalo
1Hospital for Sick Children Research Institute and University of Toronto
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 1, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1261, August 2008
Publication History:
- Published Online:
- 2008-08-02
Keywords: cluster analysis; eigenanalysis; microarray; segmented regression; scree plot
Comments (2)
why I cannot download it?
posted by: houyan jiang on 2012-05-24 09:53 AM (Europe/Berlin)
Could you please tell us a bit more about the download problem? Did you get an error message?
posted by: De Gruyter Online on 2012-05-24 10:49 AM (Europe/Berlin)