Inferring decision trees using the minimum description length principle
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Heterogeneous Learner for Web Page Classification
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Effectiveness of fuzzy discretization for class association rule-based classification
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Effective classification by integrating support vector machine and association rule mining
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 0.03 |
In this study, we propose a new technique to integrate support vector machine and association rule mining in order to implement a fast and efficient classification algorithm that overcomes the drawbacks of machine learning and association rule-based classification algorithms. The reported test results demonstrate the applicability, efficiency and effectiveness of the proposed approach.