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

Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers