Clusters with core-tail hierarchical structure and their applications to machine learning classification

We present a method for analysis of clustering results. This method represents every cluster as a stratified hierarchy of its subsets of objects (strata) ordered along a scale of their internal similarities. The "layered structures" can be described as a tool for interpretation of individual clusters rather than for describing the model of the entire data. It can be used not only for comparisons of different clusters, but also for improving existing methods to get "good" clusters. We show that this approach can also be used for improving supervised machine learning methods, particularly "active machine learning" methods, by specific analysis and preprocessing of a training data.