Abstract
Associative classification is a well-known technique which uses association rules to predict the class label for new data object. This model has been recently reported to achieve higher accuracy than traditional classification approaches. There are various strategies for good associative classification in its three main phases: rules generation, rules pruning and classification. Based on a systematic study of these strategies, we propose a new framework named MCRAC, i.e., M ining C orrelated R ules for A ssociative C lassification. MCRAC integrates the advantages of the previously proposed effective strategies as well as the new strategies presented in this paper. An extensive performance study reveals that the advantages of the strategies and the improvement of MCRAC outperform other associative classification approaches on accuracy.
This work is supported by the National Natural Science Foundation of China (60205007), Natural Science Foundation of Guangdong Province (031558, 04300462), Research Foundation of National Science and Technology Plan Project (2004BA721A02), Research Foundation of Science and Technology Plan Project in Guangdong Province (2003C50118) and Research Foundation of Science and Technology Plan Project in Guangzhou City (2002Z3-E0017).
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Chen, J., Yin, J., Huang, J. (2005). Mining Correlated Rules for Associative Classification. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_16
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DOI: https://doi.org/10.1007/11527503_16
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