Titlebook: Rough Set and Knowledge Technology; 5th International Co Jian Yu,Salvatore Greco,Andrzej Skowron Conference proceedings 2010 Springer-Verla

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Knowledge Reduction in Random Incomplete Information Systems via Evidence Theoryamined. It is proved that, in a random incomplete information system, an attribute set is a random belief reduct if and only if it is a classical reduct, and a random plausibility consistent set must be a consistent set.

Melanoma

Knowledge Reduction Based on Granular Computing from Decision Information Systemsion information systems. By using radix sorting and hash methods, the object granules as basic processing elements were employed to investigate knowledge reduction. The proposed method can be applied to both consistent and inconsistent decision information systems.

Chronological

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Some Comparative Analyses of Data in the RSDS System system. The results may be useful for determining the structure of research groups related to the rough sets, the scientific interests of the members of those groups, mutual collaboration between groups, as well as for identifying the trends appearing in research.

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Poset Approaches to Covering-Based Rough Setsupset and downset based on a poset in a covering, studies the relationship between reducible element and downset, and obtains some good results such as sufficient and necessary condition about the reducible element in a covering.

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Generate (F, ,)-Dynamic Reduct Using Cascading Hashescan be generated in O(...) time with O(.) space where . and . are total number of attributes and total number of instances of the table. Empirical results of generating (., .)-dynamic reducts using five of ten most popular UCI datasets are presented and they are compared to the Rough Set Exploration System (.).

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