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
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Finding Association Rules Using Fast Bit Computation: Machine-Oriented Modeling
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
A New Framework to Assess Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Mining Negative Association Rules
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
On the Mining of Substitution Rules for Statistically Dependent Items
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Applied Intelligence
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Gradual elements in a fuzzy set
Soft Computing - A Fusion of Foundations, Methodologies and Applications
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
Examples, counterexamples, and measuring fuzzy associations
Fuzzy Sets and Systems
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Mining a complete set of both positive and negative association rules from large databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Comparing partitions by means of fuzzy data mining tools
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
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Data mining techniques managing imprecision are very useful to obtain meaningful and interesting information for the user. Among some other techniques, fuzzy association rules have been developed as a powerful tool for dealing with imprecision in databases and offering a good representation of found knowledge. In this paper we introduce a formal model for managing the imprecision in fuzzy transactional databases using the restriction level representation theory, a recent representation of imprecision that extends that of fuzzy sets. This theory introduces some new operators, keeping the usual crisp properties even when negation is involved. The model allows us to mine fuzzy association rules in a straightforward way, extending the accuracy measures from the crisp case. In addition, we introduce several ways of representing and summarizing the obtained results, in order to offer new and very interesting semantics. As an application, we present how to extract fuzzy association rules involving both the presence and the absence of items using the proposed model, and we also perform some experiments with real fuzzy transactional datasets.