A Hierarchical Clustering Algorithm Based on Silhouette Index for Cancer Subtype Discovery from Omics Data

Cancer subtype discovery from omics data requires techniques to estimate the number of natural clusters in the data. Automatically estimating the number of clusters has been a challenging problem in Machine Learning. Using clustering algorithms together with internal cluster validity indexes have been a popular method of estimating the number of clusters in biomolecular data. We propose a Hierarchical Agglomerative Clustering algorithm, named SilHAC, which can automatically estimate the number of natural clusters and can find the associated clustering solution. SilHAC is parameterless. We also present two hybrids of SilHAC with Spectral Clustering and K-Means respectively as components. SilHAC and the hybrids could find reasonable estimates for the number of clusters and the associated clustering solution when applied to a collection of cancer gene expression datasets. The proposed methods are better alternatives to the clustering algorithm - internal cluster validity index pipelines for estimating the number of natural clusters.