Abstract-Positive and negative association rules are important to find useful information hidden in large datasets, especially negative association rules can reflect mutually exclusive correlation among items. Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there has been an increasing demand for mining the infrequent items. In this paper, we propose a tree based approach to store both frequent and infrequent itemsets to mine both the positive and negative association rules from frequent and infrequent itemsets. It minimizes I/O overhead by scanning the database only once. The performance study shows that the proposed method is an efficient than the previously proposed method.
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