Associations and rules in data mining: a linkage analysis

We discuss a problem of synthesis and analysis of granular rules emerging in data mining. Two descriptors of the rules (that is relevance and consistency) being viewed individually and en block are introduced. The relevance of the rules is quantified in terms of the data being covered by the antecedents and conclusions standing there. While this index describes each rule individually, the consistency of the rule deals with the quality of the rule viewed vis-a-vis other rules. It expresses how much the rule "interacts" with others in the sense that its conclusion is distorted by the conclusion parts coming from other rules. We show how the rules are formed by means of fuzzy clustering and their quality can be evaluated in terms of the above indexes. Global characteristics of a set of rules are also discussed and related to the number of information granules being constructed in the data space.