Абстрактный

Approach for Rule Pruning in Association Rule Mining for Removing Redundancy

Ashwini Batbarai, Devishree Naidu

In Data Mining Association rule mining is an important component. It is used for prediction or decision making. Numbers of method or algorithm exist for generating association rules. These Methods generates a huge number of association rules. Some are redundant rules. Many algorithms have been proposed with the objective of solving the obstacles presented in the generation of association rules. In this paper we have given the approach for removing redundancy based on frequent closed itemset mining (FCI), and using lift as the interesting measure for gating the interesting rule and forming the non-redundant rule set based on completeness and tightness properties of rule set..