Summary
The essential problem of Knowledge Discovery in Databases is to find interesting relationships, those that are meaningful in a domain. This task may be viewed as one of searching an immense space of possible actionable concepts and relations. Because the classical knowledge discovery algorithms are not able to determine if a pattern is truly actionable for a user, we focus on a new class of action rules, called e-action rules that can be used not only for automatically analyzing discovered patterns but also for reclassifying some objects in the data from one state into another more desired state. For a quicker and more effective process of e-action rules discovery, action tree algorithm is presented. Support and confidence of the rules are proposed to prune a large number of irrelevant, spurious, and insignificant generated candidates. The algorithm is implemented as DEAR_2.2 system and it is tested on several public domain databases. The results show that actionability can be considered as a partially objective measure rather than a purely subjective one. E-Action rules are useful in many fields such as medical diagnosis and business.
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Raś, Z.W., Tsay, LS. (2008). Mining E-Action Rules, System DEAR. 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_17
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DOI: https://doi.org/10.1007/978-3-540-78488-3_17
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