Reduction about approximation spaces of covering generalized rough sets
The introduction of covering generalized rough sets has made a Substantial contribution to the traditional theory of rough sets. The notion of attribute reduction call be regarded as one of the strongest and most significant results in rough sets. However, the efforts made on attribute reduction of covering generalized rough sets are far from sufficient. In this work, covering reduction is examined and discussed. We initially construct a new reduction theory by redefining the approximation spaces and the reducts of covering generalized rough sets. This theory is applicable to all types of covering generalized rough sets, and generalizes some existing reduction theories. Moreover, the Currently insufficient reducts of covering generalized rough sets are improved by the new reduction. We then investigate in detail the procedures to get reducts of a covering. The reduction of a covering also provides a technique for data reduction in data mining. (C) 2009 Elsevier Inc, All rights reserved.
reduct approximation space covering rough set data mining granular computing