Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Post-processing strategies for improving local gene expression pattern analysis
International Journal of Data Mining and Bioinformatics
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Clustering is a major exploratory technique for gene expression data in post-genomic era. As essential tools within cluster analysis, cluster validation techniques have the potential to assess the quality of clustering results and performance of clustering algorithms, helpful to the interpretation of clustering results. In this work, the validation ability of Silhouette index, Dunn's index, Davies-Bouldin index and FOM in gene clustering was investigated with public gene expression datasets clustered by hierarchical single-linkage and average-linkage clustering, K-means and SOMs. It was made clear that Silhouette index and FOM can preferably validate the performance of clustering algorithms and the quality of clustering results, Dunn's index should not be used directly in gene clustering validation for its high susceptibility to outliers, while Davies- Bouldin index can afford better validation than Dunn's index, exception for its preference to hierarchical single-linkage clustering.