Association rule mining is one of the most important tasks in data mining. The basic concept of association rules is to mine the interesting (positive) frequent patterns from a transaction database. However, mining the negative patterns has also attracted the attention of researchers in this area. The aim of this study is to develop a new model for mining interesting negative and positive association rules out of a transactional data set. The proposed model is an integration between two algorithms, the Positive Negative Association Rule (PNAR) algorithm and the Interesting Multiple Level Minimum Supports (IMLMS) algorithm, to propose a new approach (PNAR_IMLMS) for mining both negative and positive association rules from the interesting frequent and infrequent itemsets mined by the IMLMS model. The experimental results show that the PNAR_IMLMS model provides significantly better results than the previous model.
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