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As the volume of data grows at an unprecedented rate, large-scale data mining and knowledge discovery present a tremendous challenge. Rough set theory, which has been used successfully in solving problems in pattern recognition, machine learning, and data mining, centers around the idea that a set of distinct objects may be approximated via a lower and upper bound. In order to obtain the benefits...
Rough set theory (RST) for knowledge updating have been successfully applied in data mining and it's correlative domains. As a special type of probabilistic rough set model, Variable precision rough sets (VPRS) model is an extension of RST. For an information system, the VPRS model allows a flexible approximation boundary region by using a precision variable and has a better tolerance ability for...
The description of an information system will be more accurate when objects and attributes are added to it. The approximations of a concept may change when an information system varies. No research work has been done yet on incremental updating for approximations when objects and attributes change simultaneously. In traditional rough set theory, granules are induced by equivalence classes. The granularity...
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