A Decision-Theoretic Rough Set Approach for Dynamic Data Mining


Uncertainty and fuzziness usually exist in real-life data. Approximations are utilized to describe the uncertain info approximately in rough set theory. Sure and uncertain rules are induced directly from different regions partitioned by approximations. Approximation will additional be applied to knowledge-mining-related task, $hboxe.g.$, attribute reduction. Nowadays, completely different types of data collected from completely different applications evolve with time, particularly new attributes may appear while new objects are added. This paper presents an approach for dynamic maintenance of approximations $hboxw.r.t.$ objects and attributes added simultaneously beneath the framework of call-theoretic rough set (DTRS). Equivalence feature vector and matrix are outlined first to update approximations of DTRS in numerous levels of granularity. Then, the information system is decomposed into subspaces, and therefore the equivalence feature matrix is updated in different subspaces incrementally. Finally, the approximations of DTRS are renewed during the method of updating the equivalence feature matrix. Extensive experimental results verify the effectiveness of the proposed ways.

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