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 algorithm for mining interesting quantitative association rules
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Artificial Intelligence in Medicine
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
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
On the representation, measurement, and discovery of fuzzy associations
IEEE Transactions on Fuzzy Systems
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
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The objectives of hypothesis refinement in knowledge discovery are to produce rules that more accurately model the underlying data while maintaining rule interpretability. In this paper we introduce two refinement strategies for association rules with fuzzy temporal constraints. Disjunctive generalization produces more general rules by merging adjacent constraints within a partition of the window of temporal relevance. Temporal specification uses linguistic hedges to reduce the duration of a constraint to better model the distribution of examples. Both types of refinement produce rules expressible using the linguistic terms of the original rules. The acquisition of the information needed to perform the refinements is incorporated into a general algorithm for determining the number of examples and counterexamples of rules with fuzzy temporal constraints.