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Rough set theory, proposed by Pawlak, has been proved to be a mathematical tool to deal with vagueness and uncertainty in intelligent information processing. In this paper, we propose the concept of knowledge granulation in interval-valued information systems, and discuss some important properties. From these properties, it can be shown that the proposed knowledge granulation provides important approaches...
Attribute reduction is a key task for the research of rough sets. However, when dealing with large-scale data, many existing proposals based on rough set theory get worse performance. In this paper, we propose a novel attribute reduction algorithm of decomposition based on rough sets. The idea of decomposition is to break down a complex table into a super-table and several sub-tables that are simpler,...
Variable precision rough set model, as a probabilistic extension of original rough set model, is a very useful approach to inducing probabilistic rules from datasets. In this paper, some anomalies in present definition of attribute reduction based on variable precision rough set model are discussed. Maximum condition entropy is introduced to analyze the mergers of condition classes in the process...
Quality of clustering is an important issue in application of clustering techniques. Most traditional cluster validity indices are geometry-based cluster quality measures. This paper proposes a cluster validity index based on the decision-theoretic rough set model by considering various loss functions. Experiments with synthetic, standard, and real-world retail data show the usefulness of the proposed...
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