Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Support envelopes: a technique for exploring the structure of association patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Horn axiomatizations for sequential data
Theoretical Computer Science
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
Mining Association Rule Bases from Integrated Genomic Data and Annotations
Computational Intelligence Methods for Bioinformatics and Biostatistics
Intelligent assistance for teachers in collaborative e-learning environments
Computers & Education
A new and useful syntactic restriction on rule semantics for tabular datasets
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
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Mining association rules may generate a large numbersof rules making the results hard to analyze manually.Pasquier et al. have discussed the generation of Guigues-Duquenne-Luxenburger basis (GD-L basis). Using a similarapproach, we introduce a new rule of inference anddefine the notion of association rules cover as a minimalset of rules that are non-redundant with respect to this newrule of inference. Our experimental results (obtained usingboth synthetic and real data sets) show that our coversare smaller than the GD-L basis and they are computed intime that is comparable to the classic Apriori algorithm forgenerating rules.