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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Solving Regression by Learning an Ensemble of Decision Rules
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Implementing a Rule Generation Method Based on Secondary Differences of Two Criteria
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Evaluation of a Classification Rule Mining Algorithm Based on Secondary Differences
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
IEEE Transactions on Fuzzy Systems
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DS'11 Proceedings of the 14th international conference on Discovery science
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PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Information Sciences: an International Journal
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DS'05 Proceedings of the 8th international conference on Discovery Science
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Artificial Intelligence in Medicine
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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This paper presents the APRIORI-C algorithm, modifying the association rule learner APRIORI to learn classification rules. The algorithm achieves decreased time and space complexity, while still performing exhaustive search of the rule space. Other APRIORI-C improvements include feature subset selection and rule post-processing, leading to increased understandability of rules and increased accuracy in domains with unbalanced class distributions. In comparison with learners which use the covering approach, APRIORI-C is better suited for knowledge discovery since each APRIORI-C rule has high support and confidence.