Determination of the Number of Clusters by Symmetric Non-Negative Matrix Factorization

Clustering is an unsupervised machine learning technique that serves to extract patterns in unlabeled datasets by grouping their elements based on a similarity measure. A priori knowledge of the number of clusters is needed in most of the clustering techniques, which is both difficult and necessary for an effective and accurate pattern recognition and latent (not directly observable) feature analysis. Recently, graph based Symmetric Nonnegative Matrix factorization (SymmNMF) has been demonstrated to perform better than k-means and spectral clustering. Here, we present a consensus clustering based on robust resampling technique which in conjunction with SymmNMF and Proportion of Ambiguous Clustering (PAC) criterion performs a robust graphical clustering and accurate identification of the number of clusters in several non-convex benchmark datasets.