Discovering Non-Redundant Association Rules using MinMax Approximation Rules

Frequent pattern mining is an important area of data mining used to generate the Association Rules. The extracted Frequent Patterns quality is a big concern, as it generates huge sets of rules and many of them are redundant. Mining Non-Redundant Frequent patterns is a big concern in the area of Association rule mining. In this paper we proposed a method to eliminate the redundant Frequent patterns using MinMax rule approach, to generate the quality Association Rules.