Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Heuristic Algorithm for the Set Covering Problem
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Scalable data mining for rules
Scalable data mining for rules
Direct Interesting Rule Generation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An Evaluation of Approaches to Classification Rule Selection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
On pruning and tuning rules for associative classifiers
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Direct rule generation is a possible alternative to tree building for classifiers. Here, we use the association rule framework to build classifiers. The rule generator performs direct enumeration (no generation of candidate sequences or so, and no preliminary enumeration of large sets) with online pruning to keep combinatorial explosion under control. The rule set thus generated is ultimately and drastically decimated so that a final non redundant rule system with reduced learning bias is produced. Decimation is modeled as a minimum cost and minimal set covering problem solved with a genetic algorithm. Experiment results are presented and compared to results obtained with a tree building based classifier (C4.5).