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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
International Journal of Data Mining and Bioinformatics
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
Expert Systems with Applications: An International Journal
Data mining by attribute generalization with fuzzy hierarchies in fuzzy databases
Fuzzy Sets and Systems
A CBR-based fuzzy decision tree approach for database classification
Expert Systems with Applications: An International Journal
A new approach for discovering fuzzy quantitative sequential patterns in sequence databases
Fuzzy Sets and Systems
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
Mining hesitation information by vague association rules
ER'07 Proceedings of the 26th international conference on Conceptual modeling
Association rules mining with relative weighted support
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Mining significant least association rules using fast SLP-growth algorithm
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Effect of similar behaving attributes in mining of fuzzy association rules in the large databases
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Identifying the signs of fraudulent accounts using data mining techniques
Computers in Human Behavior
Discovering fuzzy association rules with interest and conviction measures
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
A fuzzy coherent rule mining algorithm
Applied Soft Computing
A combined mining-based framework for predicting telecommunications customer payment behaviors
Expert Systems with Applications: An International Journal
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This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.