Optimal Rule Selection Scheme using Concept Relationship Analysis

In Data Mining, the Association rule mining is used to retrieve the recurrent item sets. Apriori algorithm is mainly used to mine association rules. In that, rule reduction is required for efficient decision-making system. Knowledge based rule reduction schemes are used to filter the interested rules. In the existing system rule validation is not provided. Quantitative attributes are not considered in the post-mining scheme. Weighted rule mining scheme is not supported. This paper proposes Weighted Rule mining approach to perform post mining on derived rules with ontology support. Post mining schemes are used to filter consequent rules. Based on the Support and confidence values, the interested rules are selected rules and the same is used for the decision making process. Here, rule-mining scheme is improved to handle quantitative attributes. The WARM method is improved with validation methods. Then weighted rule mining and filtering process can be incorporated with the ARIPSO scheme. And also the rank based concept relationship analysis can be provided to improve the post mining process. Ontology based Association rule mining and Ontology based weighted Rule mining comparative analysis are focused.