  After data selection, pre-processing, transformation, and feature extraction,
knowledge extraction is not the final step in a data mining process. It is then
necessary to understand this knowledge in order to apply it efficiently and
effectively. Up to now, there is a lack of appropriate techniques that support
this significant step. This is partly due to the fact that the assessment of
knowledge is often highly subjective, e.g., regarding aspects such as novelty
or usefulness. These aspects depend on the specific knowledge and requirements
of the data miner. There are, however, a number of aspects that are objective
and for which it is possible to provide appropriate measures. In this article
we focus on classification problems and use probabilistic generative
classifiers based on mixture density models that are quite common in data
mining applications. We define objective measures to assess the
informativeness, uniqueness, importance, discrimination, representativity,
uncertainty, and distinguishability of rules contained in these classifiers
numerically. These measures not only support a data miner in evaluating results
of a data mining process based on such classifiers. As we will see in
illustrative case studies, they may also be used to improve the data mining
process itself or to support the later application of the extracted knowledge.
