Abstract
The increased competition faced by today’s companies can wield data mining tools to extract actionable knowledge and then use it as a weapon to outmaneuver competitors and boost revenue. Mining reclassification rules is a way to model actionable patterns directly from a given data set. The previous work on reclassification rule mining has shown that they are effective when variables are weakly correlated. However, when the data set is correlated, some redundant rules are in the result set. This problem becomes critical for discovering rules in correlated data which may have long frequent factor-sets. In this paper, we investigate properties of reclassification rules and offer a new method to discovery a set of non-redundant reclassification rules without information loss.
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Tsay, LS., Im, S. (2009). Mining Non-redundant Reclassification Rules. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_82
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DOI: https://doi.org/10.1007/978-3-642-02568-6_82
Publisher Name: Springer, Berlin, Heidelberg
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