Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Confidence-Lift Support Specification for Interesting Associations Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Data Mining and Knowledge Discovery
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
Domain Driven Data Mining (D3M)
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Intrusion detection using fuzzy association rules
Applied Soft Computing
Mining direct and indirect weighted fuzzy association rules in large transaction databases
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Domain-Driven Data Mining: Challenges and Prospects
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Flexible Frameworks for Actionable Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
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
Applying cluster-based fuzzy association rules mining framework into EC environment
Applied Soft Computing
IEEE Transactions on Fuzzy Systems
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In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose common-sense knowledge which may not be interesting in business point of view. In this paper, an algorithm for mining fuzzy coherent rules is proposed for overcoming those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy the four criteria or not. If yes, it is a fuzzy coherent rule. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.