An efficient approach for mining positive and negative association rules from large transactional databases

In data mining association rule mining play vital role in finding associations between items in a dataset by mining essential patterns in a large database. Standard association rules consider only items present in dataset transactions. These types of rules are called as positive association rules. The other kind of rules called Negative association rules also consider the same items, but in addition considers negated items which are not present in dataset transactions. These two association rules are important in market-basket analysis to identify correlations that conflict with each other or correlations that complement each other. In this paper, we propose an algorithm that mines positive and negative association rules without adding any additional measure and extra database scans.