Positive and Negative Association Rule Mining Using Correlation Threshold and Dual Confidence Approach

Association Rule Generation has reformed into an important area in the research of data mining. Association rule mining is a significant method to discover hidden relationships and correlations among items in a set of transactions. It consists of finding frequent itemsets from which strong association rules of the form A => B are generated. These rules are used in classification, cluster analysis and other data mining tasks. This paper presents an extensive approach to the traditional Apriori algorithm for generating positive and negative rules. However, the general approaches based on the traditional support–confidence framework may cause to generate a large number of contradictory association rules. In order to solve such problems, a correlation coefficient is determined and augmented to the mining algorithm for generating association rules. This algorithm is known as the Positive and Negative Association Rules generating (PNAR) algorithm. An improved PNAR algorithm is proposed in this paper. The experimental result shows that the algorithm proposed in this paper can reduce the degree of redundant and contradictory rules, and generate rules which are interesting on the basis of a correlation measure and dual confidence approach.