Finding association rules in transaction databases is most commonly seen in data mining. In real applications, different items may have different support criteria to judge their importance, taxonomic relationships among items may appear, and data may have quantitative values. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in quantitative transactions with multiple minimum supports of items. Items may have different minimum supports and the maximum-itemset minimum-taxonomy support constraint is adopted to discover the large itemsets. Under the constraint, the characteristic of downward-closure is kept, such that the original apriori algorithm can be easily extended to find fuzzy large itemsets. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. It can also discover cross-level fuzzy association rules under the maximum-itemset minimum-taxonomy support constraint. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under multiple item supports in a simple and effective way.
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