Mining fuzzy association rules with multiple minimum supports using maximum constraints

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items or itemsets and identify the relationships among transactions using binary values. But in real applications, different items may have different criteria to judge its importance and quantitative data may exist. In this paper, we thus propose a fuzzy mining algorithm for discovering useful fuzzy association rules under the maximum support constraints. Items may have different minimum supports and the minimum support for an itemset is set as the maximum of the minimum supports of the items contained in the itemset. Under the constraint, the characteristic of level-by-level processing is kept, such that the original Apriori algorithm can be easily extended to find the large itemsets. An example is also given to illustrate the proposed algorithm.