Titlebook: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing; 13th International C Sergei O. Kuznetsov,Dominik Ślęzak,Boris G. Mirkin Confere

Gingivitis

Femine

Covering Numbers in Covering-Based Rough Setsents are important in rough sets. For example, information entropy, knowledge dependency are useful in attribute reduction algorithms. This paper proposes the concepts of the lower and upper covering numbers to establish measurements in covering-based rough sets which are generalizations of rough se

BLUSH

Commentary

ONYM

Optimization and Adaptation of Dynamic Models of Fuzzy Relational Cognitive Mapsf optimal (in some sense) parameters of these models, in particular such quantities as linguistic variables and fuzzy relations. Two-stage approach was applied to the optimization issue, composed of conditional optimization and adaptation. Certain results of simulation research concerning the design

molest

Sensitivity Analysis for Fuzzy Linear Programming Problems method is proposed for solving same type of problems. The main advantage of proposed method over the existing method is that if the same problems are solved by both the existing as well as proposed method then the obtained results will be same, while, it is easy and less time consuming to apply the

bleach

modifier

CRAB

使高兴

Dual Rough Approximations in Information Tables with Missing Valuesfree from the restriction that missing values may occur to only some specified attributes. Hence, we can use the method of possible equivalence classes to obtain rough approximations between arbitrary sets of attributes having missing values.