Abstract
Class association rule mining is one of the most important studies supporting classification and prediction. Multiple researches recently focus on mining class association rules using support and confidence user-defined thresholds. However, in the real datasets, each attribute is associated with an indicator value. Based on the actual needs, in this paper, we propose a new approach which combines support, confidence and an interestingness measure (weight) to quickly improve the accuracy of class association rules.
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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2015.10.
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Nguyen, L.T.T., Vo, B., Mai, T., Nguyen, TL. (2018). A Weighted Approach for Class Association Rules. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_18
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DOI: https://doi.org/10.1007/978-3-319-76081-0_18
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