  Discovering frequent itemset is a key difficulty in significant data mining
applications, such as the discovery of association rules, strong rules,
episodes, and minimal keys. The problem of developing models and algorithms for
multilevel association mining poses for new challenges for mathematics and
computer science. In this paper, we present a model of mining multilevel
association rules which satisfies the different minimum support at each level,
we have employed princer search concepts, multilevel taxonomy and different
minimum supports to find multilevel association rules in a given transaction
data set. This search is used only for maintaining and updating a new data
structure. It is used to prune early candidates that would normally encounter
in the top-down search. A main characteristic of the algorithms is that it does
not require explicit examination of every frequent itemsets, 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
