Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Vague sets or intuitionistic fuzzy sets for handling vague data: which one is better?
ER'05 Proceedings of the 24th international conference on Conceptual Modeling
Maintaining consistency of vague databases using data dependencies
Data & Knowledge Engineering
Mining hesitation information by vague association rules
ER'07 Proceedings of the 26th international conference on Conceptual modeling
Mining uncertain data with probabilistic guarantees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating probabilistic frequent itemset mining: a model-based approach
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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In many online shopping applications, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold. For example, those items that are put into the basket but not checked out. We say that those almost sold items carry hesitation information since customers are hesitating to buy them. The hesitation information of items is valuable knowledge for the design of good selling strategies. We apply vague set theory in the context of AR mining as to incorporate the hesitation information into the ARs. We define the concepts of attractiveness and hesitation of an item, which represent the overall information of a customer's intent on an item. Based on these two concepts, we propose the notion of Vague Association Rules (VARs) and devise an efficient algorithm to mine the VARs. Our experiments show that our algorithm is efficient and the VARs capture more specific and richer information than traditional ARs.