The Science and Information (SAI) Organization
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, the authors 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.