  The problem of developing models and algorithms for multilevel association
mining pose for new challenges for mathematics and computer science. These
problems become more challenging, when some form of uncertainty like fuzziness
is present in data or relationships in data. This paper proposes a multilevel
fuzzy association rule mining models for extracting knowledge implicit in
transactions database with different support at each level. The proposed
algorithm adopts a top-down progressively deepening approach to derive large
itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary
intervals. An example is also given to demonstrate that the proposed mining
algorithm can derive the multiple-level association rules under different
supports in a simple and effective manner.
