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Dynamic data, in which the values of objects vary over time, are ubiquitous in real applications. Although researchers have developed a few incremental attribute reduction algorithms to process dynamic data, the reducts obtained by these algorithms are usually not optimal. To overcome this deficiency, in this paper, we propose a discernibility matrix based incremental attribute reduction algorithm,...
Rough set theory is one of important tools of soft computing, and rough approximations are the essential elements in rough set models. However, the existing fuzzy rough set model for set-valued data, which is directly constructed based on a kind of similarity relation, fail to explicitly define fuzzy rough approximations. To solve this issue, in this paper, we propose two types of fuzzy rough approximations,...
Attribute reduction plays an important role in data mining based on concept lattice theory, which makes knowledge discovery from data easier and knowledge representation simpler. However, many existing methods often employ a discernibility matrix to calculate a set of complete reducts and are time-consuming. To solve this problem, in this paper, based on an expanded concept lattice, called closed...
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