Frequent absence and presence itemset for negative association rule mining

Abstract

Negative association rule (NAR) mining has created more attention recently due to the knowledge and discovery of the interestingness of the pattern of the negative association rules and the challenges during the mining process. Pattern from negative association rules are considered to be unique and unexpected compared to positive rules. Negative association rules are useful in analysis for decision making in identifying the items which conflict with each other or the items that complement each other. However, negative association rules mining still have their own issues such as mining space and good quality of negative association rules. In this paper, we provide the preliminaries of basic concepts of negative association rule. We proposed an enhancement in Apriori algorithm for mining negative association rule from frequent absence and presence (FAP) itemset. Prominent literature will be discussed to further understand negative association rule mining.