Verbal Characterization of Probabilistic Clusters using Minimal
  Discriminative Propositions
  In a knowledge discovery process, interpretation and evaluation of the mined
results are indispensable in practice. In the case of data clustering, however,
it is often difficult to see in what aspect each cluster has been formed. This
paper proposes a method for automatic and objective characterization or
"verbalization" of the clusters obtained by mixture models, in which we collect
conjunctions of propositions (attribute-value pairs) that help us interpret or
evaluate the clusters. The proposed method provides us with a new, in-depth and
consistent tool for cluster interpretation/evaluation, and works for various
types of datasets including continuous attributes and missing values.
Experimental results with a couple of standard datasets exhibit the utility of
the proposed method, and the importance of the feedbacks from the
interpretation/evaluation step.
