Effective Mining of Fuzzy Multi-Cross-Level Weighted Association Rules

This paper addresses fuzzy weighted multi-cross-level association rule mining. We define a fuzzy data cube, which facilitates for handling quantitative values of dimensional attributes, and hence allows for mining fuzzy association rules at different levels. A method is introduced for single dimension fuzzy weighted association rules mining. To the best of our knowledge, none of the studies described in the literature considers weighting the internal nodes in such taxonomy. Only items appearing in transactions are weighted to find more specific and important knowledge. But, sometimes weighting internal nodes on a tree may be more meaningful and enough. We compared the proposed approach to an existing approach that does not utilize fuzziness. The reported experimental results demonstrate the effectiveness and applicability of the proposed fuzzy weighted multi-cross-level mining approach.