Abstract
Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.
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Acknowledgment
I. Fister Jr. and I. Fister acknowledge the financial support from the Slovenian Research Agency (Research Core Fundings No. P2-0041 and P2-0057). A. Iglesias and A. Galvez acknowledge the financial support from the projects #TIN2017-89275-R (AEI/FEDER, UE), and #JU12 (SODERCAN/FEDER UE). E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program.
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Fister, I., Iglesias, A., Galvez, A., Del Ser, J., Osaba, E., Fister, I. (2018). Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_9
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