This paper extends the work on discovering fuzzy association rules with degrees of support and implication (ARsi). The effort is twofold: one is to discover ARsi with hierarchy so as to express more semantics due to the fact that hierarchical relationships usually exist among fuzzy sets associated with the attribute concerned; the other is to generate a “core” set of rules, namely the rule cover set, that are of more interest in a sense that all other rules could be derived by the cover set. Corresponding algorithms for ARsi with hierarchy and the cover set are proposed along with pruning strategies incorporated to improve the computational efficiency. Some data experiments are conducted as well to show the effectiveness of the approach.
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