C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient mining of association rules using closed itemset lattices
Information Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
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
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Prince: an algorithm for generating rule bases without closure computations
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and 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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Associative classification is a promising new approach that mainly uses association rule mining in classification. However, most associative classification approaches suffer from the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. In this paper, a new associative classification approach called Garc is proposed. Garc takes advantage of generic basis of association rules in order to reduce the number of association rules without jeopardizing the classification accuracy. Furthermore, since rule ranking plays an important role in the classification task, GARC proposes two different strategies. The latter are based on some interestingness measures that arise from data mining literature. They are used in order to select the best rules during classification of new instances. A detailed description of this method is presented, as well as the experimentation study on 12 benchmark data sets proving that Garc is highly competitive in terms of accuracy in comparison with popular classification approaches.