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Neighborhood Rough Sets for Dynamic Data Mining
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DOI：10.1002/int.21523.png)
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Abstract
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Approximations of a concept in rough set theory induce rules and need to update for dynamic data mining and related tasks. Most existing incremental methods based on the classical rough set model can only be used to deal with the categorical data. This paper presents a new dynamic method for incrementally updating approximations of a concept under neighborhood rough sets to deal with numerical data. A comparison of the proposed incremental method with a nonincremental method of dynamic maintenance of rough set approximations is conducted by an extensive experimental evaluation on different data sets from UCI. Experimental results show that the proposed method effectively updates approximations of a concept in practice. (C) 2012 Wiley Periodicals, Inc.
Keywords:
ATTRIBUTE REDUCTION
FEATURE-SELECTION
KNOWLEDGE
GRANULATION
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