On the minimization of concave information functionals for unsupervised classification via decision trees

A popular method for unsupervised classification of high-dimensional data via decision trees is characterized as minimizing the empirical estimate of a concave information functional. It is shown that minimization of such functionals under the true distributions leads to perfect classification.