Determining the optimal number of clusters through Symmetric Non-Negative Matrix Factorization

Cluster analysis, as a form of unsupervised learning, has been developed to group observations by leveraging application-specific similarity measures. This study investigates matrix factorization techniques, with a specific focus on analyzing lexical tables within the framework of term-document matrices. Symmetric Non- Negative Matrix Factorization (SNMF) takes center stage as an effective tool for clustering operations. The primary challenge addressed is the automated determination of the optimal number of clusters.