k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation
arXiv:2409.06938v11 citationsh-index: 1
Originality Synthesis-oriented
AI Analysis
This provides a new theoretical framework for clustering, but appears incremental as it adapts existing concepts to likelihood.
The paper tackled the problem of hard clustering by developing a likelihood-based approach instead of distance-based methods, and proved convergence with simulations and real data examples.
We develop hard clustering based on likelihood rather than distance and prove convergence. We also provide simulations and real data examples.