  Finding multilevel association rules in transaction databases is most
commonly seen in is widely used in data mining. In this paper, we present a
model of mining multilevel association rules which satisfies the different
minimum support at each level, we have employed fuzzy set concepts, multi-level
taxonomy and different minimum supports to find fuzzy multilevel association
rules in a given transaction data set. Apriori property is used in model to
prune the item sets. The proposed model adopts a topdown progressively
deepening approach to derive large itemsets. This approach incorporates fuzzy
boundaries instead of sharp boundary intervals. An example is also given to
demonstrate and support that the proposed mining algorithm can derive the
multiple-level association rules under different supports in a simple and
effective manner.
