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Rule extraction is an important issue of data mining and many efficient algorithms based on rough sets have been presented for obtaining rules from decision tables. However, little work has been focused on extracting rules from the incomplete decision tables. In this paper based on an improved discernibility matrix an efficient method for obtaining all optimal credible decision rules from an incomplete...
Attribute reduction is an important issue of data mining. In this paper an incremental reduct algorithm is proposed for incomplete decision tables. A reduct definition is firstly presented. And then based on the concept of generalized decision the different cases caused by adding a new object to an incomplete decision table are deeply analyzed and some important conclusions are proved by theorems...
Core is an important concept of rough set theory and many efficient reduction methods were proposed from the computation of core. However, very little work has been performed on updating a core, especially for incomplete decision tables. In this paper an incremental algorithm for updating the core of an incomplete decision table is presented based on discernibility matrix, which may obtain a new core...
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