Abstract
Association rule mining is an important data mining method primarily used for market basket analysis. However, the method usually generates a large number of association rules; and it is difficult to use domain-independent objective measures to help find pragmatically important rules. To address these issues, we present a general method that succinctly summarizes rules with common consequent(s). This consequent-based approach allows user to focus on evaluating a rule set based on the practical significance of consequent(s) in an application domain, which usually outweighs the importance of objective measures such as rule confidence. We provide a case study to demonstrate how the proposed method can be used in conjunction with a heuristic procedure to find important rules generated from large real-world data, leading to discovery of important business knowledge and insights.
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Tan, S.C., Sim, B.H. (2014). A Pragmatic Approach to Summarize Association Rules in Business Analytics Projects. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_30
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DOI: https://doi.org/10.1007/978-3-319-13987-6_30
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