  Clustering stands as one of the most prominent challenges within the realm of
unsupervised machine learning. Among the array of centroid-based clustering
algorithms, the classic $k$-means algorithm, rooted in Lloyd's heuristic, takes
center stage as one of the extensively employed techniques in the literature.
Nonetheless, both $k$-means and its variants grapple with noteworthy
limitations. These encompass a heavy reliance on initial cluster centroids,
susceptibility to converging into local minima of the objective function, and
sensitivity to outliers and noise in the data. When confronted with data
containing noisy or outlier-laden observations, the Median-of-Means (MoM)
estimator emerges as a stabilizing force for any centroid-based clustering
framework. On a different note, a prevalent constraint among existing
clustering methodologies resides in the prerequisite knowledge of the number of
clusters prior to analysis. Utilizing model-based methodologies, such as
Bayesian nonparametric models, offers the advantage of infinite mixture models,
thereby circumventing the need for such requirements. Motivated by these facts,
in this article, we present an efficient and automatic clustering technique by
integrating the principles of model-based and centroid-based methodologies that
mitigates the effect of noise on the quality of clustering while ensuring that
the number of clusters need not be specified in advance. Statistical guarantees
on the upper bound of clustering error, and rigorous assessment through
simulated and real datasets suggest the advantages of our proposed method over
existing state-of-the-art clustering algorithms.
