Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
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Associative classification has been recently proposed which combines association rule mining and classification, and many studies have shown that associative classifiers give high prediction accuracies compared with other traditional classifiers such as a decision tree. However, in order to apply association rule mining to classification problems, data transformation into the form of transaction data should be preceded before applying association rule mining. In this paper, we propose a discretization method based on Support vector machines, which can improve the performance of association classification greatly. The proposed method finds optimal class boundaries by using SVM, and discretization utilizing distances to the boundaries is performed. Experimental results demonstrate that performing SVM-based discretization for continuous attributes makes associative classification more effective in that it reduces the number of classification rules mined and also improves the prediction accuracies at the same time.