C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 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
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Abstract-Driven Pattern Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Algorithm of Mining Class Association Rules
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
About the lossless reduction of the minimal generator family of a context
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Generic association rule bases: are they so succinct?
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
A fast pruning redundant rule method using Galois connection
Applied Soft Computing
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
An evolutionary approach to rank class association rules with feedback mechanism
Expert Systems with Applications: An International Journal
Efficient Search Methods for Statistical Dependency Rules
Fundamenta Informaticae - Machine Learning in Bioinformatics
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
Information Sciences: an International Journal
Sentiment classification of web review using association rules
OCSC'13 Proceedings of the 5th international conference on Online Communities and Social Computing
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here, we present a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery schemes such as nonredundant association rule discovery. We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure and implementation. Optimal rule discovery is an efficient alternative to association rule discovery, especially when the minimum support is low.