A new Informative Generic Base of Association Rules

The problem of the relevance and the usefulness of extracted association rules is becoming of primary importance, since an overwhelm- ing number of association rules may be derived from even reasonably sized real-life databases. In this paper, we introduce a novel generic base of association rules, based on the Galois connection semantics. The novel generic base is sound and informative. We also present a sound axiomatic system, allowing to derive all association rules that can be drawn from an extraction context.

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