Effectivity of Internal Validation Techniques for Gene Clustering

Abstract

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.