A Novel Theoretical Analysis for Clustering Heteroscedastic Gaussian Data without Knowledge of the Number of Clusters

A Novel Theoretical Analysis for Clustering Heteroscedastic Gaussian Data without Knowledge of the Number of Clusters explores A theoretical framework for clustering heteroscedastic Gaussian data with a novel cost function and Wald kernel, leading to a new algorithm called CENTRE-X.. Commercial viability score: 3/10 in Clustering Algorithms.