  Hierarchical clustering is one of the most powerful solutions to the problem
of clustering, on the grounds that it performs a multi scale organization of
the data. In recent years, research on hierarchical clustering methods has
attracted considerable interest due to the demanding modern application
domains.
  We present a novel divisive hierarchical clustering framework called
Hierarchical Stochastic Clustering (HSC), that acts in two stages. In the first
stage, it finds a primary hierarchy of clustering partitions in a dataset. In
the second stage, feeds a clustering algorithm with each one of the clusters of
the very detailed partition, in order to settle the final result. The output is
a hierarchy of clusters. Our method is based on the previous research of Meyer
and Weissel Stochastic Data Clustering and the theory of Simon and Ando on
Variable Aggregation.
  Our experiments show that our framework builds a meaningful hierarchy of
clusters and benefits consistently the clustering algorithm that acts in the
second stage, not only computationally but also in terms of cluster quality.
This result suggest that HSC framework is ideal for obtaining hierarchical
solutions of large volumes of data.
