Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Personalization on the Net using Web mining: introduction
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Mining soft-matching association rules
Proceedings of the eleventh international conference on Information and knowledge management
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Evaluation of web usage mining approaches for user's next request prediction
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Information Sciences—Informatics and Computer Science: An International Journal
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Enriching the ER model based on discovered association rules
Information Sciences: an International Journal
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
Examples, counterexamples, and measuring fuzzy associations
Fuzzy Sets and Systems
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Fuzzy versus quantitative association rules: a fair data-driven comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
In Defense of Fuzzy Association Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
On the representation, measurement, and discovery of fuzzy associations
IEEE Transactions on Fuzzy Systems
Electronic promotion to new customers using mkNN learning
Information Sciences: an International Journal
Simulation of fuzzy random variables
Information Sciences: an International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection
Journal of Intelligent Manufacturing
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In item promotion applications, there is a strong need for tools that can help to unlock the hidden profit within each individual customer's transaction history. Discovering association patterns based on the data mining technique is helpful for this purpose. However, the conventional association mining approach, while generating ''strong'' association rules, cannot detect potential profit-building opportunities that can be exposed by ''soft'' association rules, which recommend items with looser but significant enough associations. This paper proposes a novel mining method that automatically detects hidden profit-building opportunities through discovering soft associations among items from historical transactions. Specifically, this paper proposes a relaxation method of association mining with a new support measurement, called soft support, that can be used for mining soft association patterns expressed with the ''most'' fuzzy quantifier. In addition, a novel measure for validating the soft-associated rules is proposed based on the estimated possibility of a conditioned quantified fuzzy event. The new measure is shown to be effective by comparison with several existing measures. A new association mining algorithm based on modification of the FT-Tree algorithm is proposed to accommodate this new support measure. Finally, the mining algorithm is applied to several data sets to investigate its effectiveness in finding soft patterns and content recommendation.