A Novel Maximum Distribution Reduction Algorithm for Inconsistent Decision Tables
A maximum distribution reduction is meant to preserve not only all deterministic information with respect to decision attributes but also the largest possible decision class for each object of an inconsistent decision table. Hence, it is useful to compute this type of reduction when mining decision tables with data inconsistency. This paper presents a novel algorithm for finding a maximum distribution reduct of an inconsistent decision table. Two functions of attribute sets are introduced to characterize a maximum distribution reduct in a new and simple way and then used as a heuristic in the algorithm to search for a reduction. Complexity analysis of the algorithm is also presented. As an application example, the presented algorithm was applied to mine a real surgery database and some interesting results were obtained.
KeywordsDecision Table Decision Attribute Decision Class Maximum Distribution Discernibility Matrix
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