Validating clustering for gene expression data

2nd International Conference on Computer and Automation Engineering (ICCAE 2010). It is shown that this measure is an improvement over the figure of merit, an existing validation measure especially developed for clusterings of gene expression data. This measure also useful to estimate missing gene expression levels, based the similarity information contained in a given clustering.We call the new methodology , and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters.The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions.This measure also useful to estimate missing gene expression levels, based the similarity information contained in a ... show more We propose a measure for the validation of clusterings of gene expression data. For further information, including about cookie settings, please read our Cookie Policy .By continuing to use this site, you consent to the use of cookies.

ER - We propose a measure for the validation of clusterings of gene expression data.

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We found our quantitative measures of cluster quality to be positively correlated with external standards of cluster quality.

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