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Published in:
2009 | OriginalPaper | Chapter
Evolutionary Extraction of Association Rules: A Preliminary Study on their Effectiveness
Authors : Nicolò Flugy Papè, Jesús Alcalá-Fdez, Andrea Bonarini, Francisco Herrera
Published in: Hybrid Artificial Intelligence Systems
Publisher: Springer Berlin Heidelberg
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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.