Abstract
Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transactions, however the data in real-world applications usually consists of quantitative values. In the last few years, many researchers have proposed Evolutionary Algorithms for mining interesting association rules from quantitative data. In this paper, we present a preliminary study on the evolutionary extraction of quantitative association rules. Experimental results on a real-world dataset show the effectiveness of this approach.
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Papè, N.F., Alcalá-Fdez, J., Bonarini, A., Herrera, F. (2009). Evolutionary Extraction of Association Rules: A Preliminary Study on their Effectiveness. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_78
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DOI: https://doi.org/10.1007/978-3-642-02319-4_78
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