Mining positive and Negative Association Rules from interesting frequent and infrequent itemsets

Abstract

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.