Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Sanguinetti, Guido
IMPACT FACTOR 2018: 0.536
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Source Normalized Impact per Paper (SNIP) 2018: 0.342
Mathematical Citation Quotient (MCQ) 2018: 0.02
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.
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