Legitimate Approach to Association Rules under Incompleteness
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Direct and Incremental Computing of Maximal Covering Rules
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Datascape Survey Using the Cascade Model
DS '02 Proceedings of the 5th International Conference on Discovery Science
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Reducing redundancy in characteristic rule discovery by using integer programming techniques
Intelligent Data Analysis
Minimum-Size Bases of Association Rules
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Deduction Schemes for Association Rules
DS '08 Proceedings of the 11th International Conference on Discovery Science
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Mining multi-class datasets using genetic relation algorithm for rule reduction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Generic association rule bases: are they so succinct?
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Extracting compact and information lossless sets of fuzzy association rules
Fuzzy Sets and Systems
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
GARC: a new associative classification approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Journal of Computational Methods in Sciences and Engineering
TNS: mining top-k non-redundant sequential rules
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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