Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Efficient Discovery of Functional Dependencies and Armstrong Relations
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Data Mining Based on Formal Concept Analysis
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Comparing association rules and decision trees for disease prediction
HIKM '06 Proceedings of the international workshop on Healthcare information and knowledge management
Horn axiomatizations for sequential data
Theoretical Computer Science
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
The Journal of Machine Learning Research
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Models for association rules based on clustering and correlation
Intelligent Data Analysis
Reducing rule covers with deterministic error bounds
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Estimating missing data in data streams
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
On the merge of factor canonical bases
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
A data imputation model in sensor databases
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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We address the problem of the usefulness and the relevance of the set of discovered association rules. Using the frequent closed item set groundwork, we propose to generate bases for association rules, that are non-redundant generating sets for all association rules.