Mining Positive and Negative Association Rules Using CoherentApproach

Abstract—In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality ofIn the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Typically, before association rules are mined, a user needs to determine a support threshold in order to obtain only the frequent item sets. Having users to determine a support threshold attracts a number of issues. We propose an association rule mining framework that does not require a pre-set support threshold. Often, the number of association rules, even though large in number, misses some interesting rules and the rules’ quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions. We propose a framework to discover domain knowledge report as coherent rules. Coherent rules are discovered based on the properties of propositional logic, and therefore, requires no background knowledge to generate them. From the coherent rules discovered, association rules can be derived objectively and directly without knowing the level of minimum support threshold required. We provide analysis of the rules compare to those discovered via the apriori. The framework is developed based on implication of propositional logic via Negative and positive association algorithm. The experiments show that our approach is able to identify meaningful association rules within an acceptable execution time. This framework develop a new algorithm based on coherent rules so that users can mine the items without domain knowledge and it can mine the items efficiently when compared to association rules.