Mining multi-cross-level fuzzy weighted association rules

This paper proposes a novel approach for mining fuzzy weighted multi-cross-level association rules by simply integrating the advantages of several concepts, including fuzziness, cross-level mining, weighted mining and linguistic terms for minimum support, minimum confidence and item importance. Experimental results conducted on a synthetic database demonstrate the importance, effectiveness and applicability of the proposed approach.