On Application of a Probabilistic K-Nearest Neighbors Model for Cluster Validation Problem

Abstract

K-Nearest Neighbors is a widely used technique for classifying and clustering data. In the current article, we address the cluster stability problem based upon probabilistic characteristics of this approach. We estimate the stability of partitions obtained from clustering pairs of samples. Partitions are presumed to be consistent if their clusters are stable. Clusters validity is quantified through the amount of K-Nearest Neighbors belonging to the point's sample. The null-hypothesis, of the well-mixed samples within the clusters, suggests Binomial Distribution of this quantity with K trials and the success probability 0.5. A cluster is represented by a summarizing index, of the p-values calculated over all cluster objects, under the null hypothesis for the alternative, and the partition quality is evaluated via the worst partition cluster. The true number of clusters is attained by the empirical index distribution having maximal suitable asymmetry. The proposed methodology offers to produce the index distributions sequentially and to assess their asymmetry. Numerical experiments exhibit a good capability of the methodology to expose the true number of clusters.