A Review of Negative and Positive Association Rule Mining with Multiple Constraint and Correlation Factor
Negative and positive association rule mining is extract needful information for large database. The generation of negative and positive rule based on interesting pattern and non-interesting pattern of database. The violation of given threshold value such as minimum support and minimum confidence generate some negative rules. The generation of association rule mining dependent some algorithm such as Apriori algorithm, FP-growth algorithm and tree based algorithm. In this paper, the authors present different approach for reduction of negative rule from association rule mining. The process of reduction and removal of association rule mining follow the multiple constraints and correlation factor of rule mining.