Mutual k-Nearest Neighbor Graph for Data Analysis: Application to Metric Space Clustering.

In this paper, we delve into the Mutual k-Nearest Neighbor Graph ( m k NNG ) and its significance in clustering and outlier detection. We present a rigorous mathematical framework elucidating its application and highlight its role in the success of various clustering algorithms. Building on Brito et al.’s findings, which link the connected components of the m k NNG to clusters under specific density bounds, we explore its relevance in the context of a wide range of density functions.