Number of Clusters in a Dataset: A Regularized K-means Approach

This paper addresses the challenge of determining the optimal number of clusters in unlabeled datasets using regularized k-means, focusing on the critical hyperparameter . It derives rigorous bounds for under the assumption of ideal, spherical clusters and explores solutions with both additive and multiplicative regularization to reduce ambiguity in clustering outcomes.