Analysis of the Mirkin’s Distance on Binary Relations for Clustering Stability

Clustering stability is a popular approach to cluster validation, where the stability of clustering solutions is evaluated across resamples to select the most stable structure. However, there are few empirical studies that analyze clustering stability methods. This paper investigates the use of the Mirkin distance for evaluating the stability of clustering solutions, across non-parametric bootstrap resamples. The proposed strategy is validated with an extensive experimental analysis, providing useful insights in clustering stability for practical applications.