Summary
The ability to discover useful knowledge hidden in large volumes of data and to act on that knowledge is becoming increasingly important in today’s competitive world. Action rules were proposed to help people analyze discovered patterns and develop a workable strategy for actions [10]. A formal definition of an action rule was independently proposed in [4]. These rules have been investigated further in [11, 12].
Action rules are constructed from certain pairs of classification rules extracted earlier from the same decision table, each one defining different preferable classes. Attributes in a database are divided into two groups: stable and flexible. Flexible attributes provide a tool for making hints to a user what changes within some values of flexible attributes are needed to re-classify group of objects, supporting action rule, to another decision class.
Classical action rules only involve flexible attributes listed in both classification rules from which an action rule is constructed. The values of the common stable attributes listed in both rules are used to create an action rule but they are not listed in the expression describing that rule. Because of that, there are many options in actual real-life implementations of them. In this chapter, we propose a new class of action rules, called E-Action rules, to solve this issue. Our experience shows that an E-Action rule is more meaningful than a classical action rule or an extended action rule [11] because it is easy to interpret, understand, and apply by users.
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Tsay, LS., Raś, Z.W. (2008). E-Action Rules. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_16
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DOI: https://doi.org/10.1007/978-3-540-78488-3_16
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