Abstract: Data mining is getting increasing acceptance in science and business areas that need to identify and represent certain dependencies between attributes. This dependency between the attributes is represented in the form of association rules. Association rule mining discovers interesting correlations between attributes in a database. All the traditional association rule mining algorithms were developed to find positive associations between attributes, i.e., A→B whereas negative association rule is an implication of the form A→⇁B, ⇁A→B, ⇁A→⇁B where A and B are database attributes, ⇁A⇁B are negations of database attributes. Here, we propose an apriori based algorithm to find the both positive and negative associations between attributes. Experimental results show the effectiveness and efficiency of the proposed algorithm without additional database scans.
Ujwala Manoj Patil and J.B. Patil, 2017. Mining Both Positive and Negative Association Rules without Extra Database Scans. Journal of Engineering and Applied Sciences, 12: 5915-5920.