k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation

TL;DR

This paper introduces a likelihood-based hard clustering framework, proves its convergence, and demonstrates its effectiveness through simulations and real data applications.

Contribution

It presents a novel likelihood-based clustering method with theoretical convergence guarantees and practical validation.

Findings

Convergence of the proposed clustering algorithm is theoretically established.

Simulations show the method's effectiveness across different scenarios.

Real data examples demonstrate practical applicability.

Abstract

We develop hard clustering based on likelihood rather than distance and prove convergence. We also provide simulations and real data examples.