Computational complexity of the problem of choosing typical representatives in a 2-clustering of a finite set of points in a metric space

Abstract

— We consider the computational complexity of one extremal problem of choosing a subset of p points from some given 2 -clustering of a fi nite set in a metric space. The chosen subset of points has to describe the given clusters in the best way from the viewpoint of some geometric criterion. This is a formalization of an applied problem of data mining which consists in fi nding a subset of typical representatives of a dataset composed of two classes based on the function of rival similarity. The problem is proved to be NP-hard. To this end, we polynomially reduce to the problem one of the well-known problems NP-hard in the strong sense, the p -median problem.