Abstract
It is highly expected that knowledge discovery and data mining (KDD) methods can extract useful and understandable knowledge from large amount of data. Action rule mining presents an approach to automatically construct relevantly useful and understandable strategies by comparing the profiles of two sets of targeted objects β those that are desirable and those that are undesirable. The discovered knowledge provides an insight of how relationships should be managed so that objects of low performance can be improved. Traditionally, it was constructed from one or two classification rules. The quality and quantity of such Action Rules depend on adopted classification methods. In this paper, we present StrategyGenerator, a new algorithm for constructing a complete set of Action Rules which satisfies specified constraints. This algorithm does not require prior extraction of classification rules. Action rules are generated directly from a database.
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Tsay, LS., RaΕ, Z.W. (2008). Discovering the Concise Set of Actionable Patterns. In: An, A., Matwin, S., RaΕ, Z.W., ΕlΔzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_19
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DOI: https://doi.org/10.1007/978-3-540-68123-6_19
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