Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
The application of certainty factors to neural computing for rule discovery
IEEE Transactions on Neural Networks
End-User Access to Multiple Sources - Incorporating Knowledge Discovery into Knowledge Management
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
Mining Text Data: Special Features and Patterns
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Modelling subjectivity in visual perception of orientation for image retrieval
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Enterprise information systems IV
Association rules applied to credit card fraud detection
Expert Systems with Applications: An International Journal
Using fuzzy data mining to evaluate survey data from olive grove cultivation
Computers and Electronics in Agriculture
A definition for fuzzy approximate dependencies
Fuzzy Sets and Systems
Using semantic data integration to create reliable rule-based systems with uncertainty
Engineering Applications of Artificial Intelligence
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
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The usual support/confidence framework to assess association rules has several drawbacks that lead to obtain many misleading rules, even in the order of 95% of the discovered rules in some of our experiments. In this paper we introduce a different framework, based on Shortliffe and Buchanan's certainty factors and the new concept of very strong rules. The new framework has several good properties, and our experiments have shown that it can avoid the discovery of misleading rules.