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
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
Journal of Computer and System Sciences
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On Local Pruning of Association Rules Using Directed Hypergraphs
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Pruning derivative partial rules during impact rule discovery
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
The Journal of Machine Learning Research
A fast pruning redundant rule method using Galois connection
Applied Soft Computing
Information Sciences: an International Journal
CBC: An associative classifier with a small number of rules
Decision Support Systems
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The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all of the rules, especially because many rules are redundant and contained in other rules. We discuss how the sparseness of the data affects the redundancy and containment between the rules and provide a new methodology for organizing and grouping the association rules with the same consequent. It consists of finding metarules, rules that express the associations between the discovered rules themselves. The information provided by the metarules is used to reorganize and group related rules. It is based only on data-determined relationships between the rules. We demonstrate the suggested approach on actual manufacturing data and show its effectiveness on several benchmark data sets.