A Penalized Criterion for Selecting the Number of Clusters for K-Medians

Abstract

Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, that is in the case where K-medians algorithm should be preferred to K-means because of its robustness. More precisely, we concentrate on a common question in clustering: how to chose the number of clusters? The answer proposed here is to consider the choice of the optimal number of clusters as the minimization of a penalized criterion. In this article, we obtain a suitable penalty shape for our criterion and derive an associated oracle-type inequality. Finally, the performance of this approach with different types of K-medians algorithms is compared on a simulation study with other popular techniques. All studied algorithms are available in the R package Kmedians on CRAN.

KEYWORDS:

• Clustering
• K-medians
• Robust statistics

Acknowledgments

The authors wish to thank Califrais for providing the real-life data and Raphaël Carpintero Perez for the data preprocessing work.

Disclosure Statement

No potential conflict of interest was reported by the author(s).