A text mining process using association rules generates a very large number of rules. According to experts of the domain, most of these rules basically convey a common knowledge, i.e. rules which associate terms that experts may likely relate to each other. In order to focus on the result interpretation and discover new knowledge units, it is necessary to define criteria for classifying the extracted rules. Most of the rule classification methods are based on numerical quality measures. In this chapter, we introduce two classification methods: The first one is based on a classical numerical approach, i.e. using quality measures, and the other one is based on domain knowledge. We propose the second original approach in order to classify association rules according to qualitative criteria using domain model as background knowledge. Hence, we extend the classical numerical approach in an effort to combine data mining and semantic techniques for post mining and selection of association rules. We mined a corpus of texts in molecular biology and present the results of both approaches, compare them, and give a discussion on the benefits of taking into account a knowledge domain model of the data.

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   |texte=   A Conformity Measure using Background Knowledge for Association Rules: Application to Text Mining
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