Verbal Characterization of Probabilistic Clusters using Minimal Discriminative Propositions [article]

Yoshitaka Kameya, Satoru Nakamura, Tatsuya Iwasaki, Taisuke Sato
2011 arXiv   pre-print
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
more » ... ed 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.
arXiv:1108.5002v2 fatcat:mxu2olge75cvniqp25tiiqp7ba