Domain driven approach for coherent rule mining

In Association Rule Mining, minimum support threshold is used to get the association rules. Deciding this threshold is quite a difficult task and has a great influence on the number and the quality of association rules. There is no chance of neglecting minimum support threshold as the large number of association rules generated missed some interesting rules discovered. The process of decision making with these rules may lead to undesirable and unexpected results. Minimum support threshold thus played a vital role in the entire process. To remove this dependency on minimum support threshold, we have proposed a framework which contains domain knowledge method, feature selection method and pruning technique to reduce the complexity of coherent algorithm to discover interesting positive and negative rules for business which are discovered based on the properties of propositional logic and thus do not require the minimum support threshold. In the initial part of the paper, we have explained the formation of coherent rules. Later, to reduce the complexity and make it more efficient we have added the feature of domain-driven to the framework of coherent rules and this feature is demonstrated with the help of implemented example. Further we have also introduced the concept of Combined Rule Mining which further enhances the results generated.