  In this paper we formulate in general terms an approach to prove strong
consistency of the Empirical Risk Minimisation inductive principle applied to
the prototype or distance based clustering. This approach was motivated by the
Divisive Information-Theoretic Feature Clustering model in probabilistic space
with Kullback-Leibler divergence which may be regarded as a special case within
the Clustering Minimisation framework. Also, we propose clustering
regularization restricting creation of additional clusters which are not
significant or are not essentially different comparing with existing clusters.
