Titlebook: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing; 10th International C Dominik Ślęzak,Guoyin Wang,Yiyu Yao Conference proceeding

fathom

书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing影响因子(影响力)




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing影响因子(影响力)学科排名




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing网络公开度




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing网络公开度学科排名




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing被引频次




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing被引频次学科排名




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing年度引用




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing年度引用学科排名




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing读者反馈




书目名称Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing读者反馈学科排名

falsehood

A Modal Characterization of Indiscernibility and Similarity Relations in Pawlak’s Information Systemrnibility, as well as weak and strong versions of forward and backward informational inclusion, as well as positive and negative similarities. . extends the logic . introduced in [4] by adding a modality corresponding to strong indiscernibility relation. The main problem in the modal treating of str

我就不公正

Granular Computing with Shadowed Setsoolean (two-valued) description of data and quantitative membership grades, we introduce an interpretation framework of shadowed sets. Shadowed sets are discussed as three-valued constructs induced by fuzzy sets assuming three values (that could be interpreted as full membership, full exclusion, and

没有贫穷

Rough Sets and Higher Order Vaguenessimation spaces in searching for concept approximation is emphasized. Boundary regions of approximated concepts within the adaptive learning framework are satisfying the higher order vagueness condition, i.e., the boundary regions of vague concepts are not crisp. There are important consequences of t

可忽略

Blanch

New Approach for Basic Rough Set Conceptswer and upper approximation operators. This approach is a generalization for Pawlak approach and the generalizations in [2, 7, 10, 12, 13, 14, 15, 16]. Properties of the suggested concepts are obtained. Also comparison between our approach and previous approaches are given. In this case, we show tha

委派

使入迷

Characterizations of Attributes in Generalized Approximation Representation Spaceson the universe are reflexive. Many information tables, such as consistent or inconsistent decision tables, variable precision rough set models, consistent decision tables with ordered valued domains and with continuous valued domains, and decision tables with fuzzy decisions, can be unified to gene

Aura231

角斗士