Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generating Linear Extensions Fast
SIAM Journal on Computing
Mining fuzzy association rules in databases
ACM SIGMOD Record
A Guided Tour of Relational Databases and Beyond
A Guided Tour of Relational Databases and Beyond
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Mining vague association rules
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Handling inconsistency of vague relations with functional dependencies
ER'07 Proceedings of the 26th international conference on Conceptual modeling
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
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
Maintaining consistency of vague databases using data dependencies
Data & Knowledge Engineering
Handling inconsistency of vague relations with functional dependencies
ER'07 Proceedings of the 26th international conference on Conceptual modeling
Prioritized preferences and choice constraints
ER'07 Proceedings of the 26th international conference on Conceptual modeling
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In many online shopping applications, such as Amazon and eBay, 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. However, there is no conceptual model that is able to capture different statuses of hesitation information. Herein, we apply and extend vague set theory in the context of AR mining. 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 the two concepts, we propose the notion of Vague Association Rules (VARs). We 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 do the traditional ARs.