Abstract
The ability to make mined patterns actionable is becoming increasingly important in today’s competitive world. Standard data mining focuses on patterns that summarize data and these patterns are required to be further processed in order to determine opportunities for action. To address this problem, it is essential to extract patterns by comparing the profiles of two sets of relevant objects to obtain useful, understandable, and workable strategies. In this paper, we present the definition of actionable rules by integrating action rules and reclassification rules to build a framework for analyzing big data. In addition, three new interestingness measures, coverage, leverage, and lift, are proposed to address the limitations of minimum left support, right support and confidence thresholds for gauging the importance of discovered actionable rules.
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Tsay, LS. (2014). Interestingness Measures for Actionable Patterns. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_27
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DOI: https://doi.org/10.1007/978-3-319-08729-0_27
Publisher Name: Springer, Cham
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