Classification of voluminous protein data based on the structural and functional properties is a challenging task for researchers in bioinformatics field. In this paper a faster, accurate and efficient classification tool Rough Set Protein Classifier has been developed which has a classification accuracy of 97.7%. It is a hybridized tool comprising Sequence Arithmetic, Rough Set Theory and Conc...

We present a survey of clustering methods based on rough set data analysis.

This paper deals with multi- objective nonlinear programming problem having rough intervals in the constraints. The problem is approached by taking maximum value range and minimum value range inequalities as constraints conditions, reduces it into two classical multi-objective nonlinear programming problems, called lower and upper approximation problems.  All of the lower and upper approximatio...

Probabilistic approaches have been applied to the theory of rough set in several forms, including decision-theoretic analysis, variable precision analysis, and information-theoretic analysis. Based on rough membership functions and rough inclusion functions, we revisit probabilistic rough set approximation operators and present a critical review of existing studies. Intuitively, they are define...

A rough set is a formal approximation of a crisp set which gives lower and upper approximation of original set to deal with uncertainties. The concept of neutrosophic set is a mathematical tool for handling imprecise, indeterministic and inconsistent data. In this paper, we defne concepts of Rough Neutrosophic algebra and investigate some of their properties.

“Our claim is that knowledge is deep–seated in the classificatory abilities of human beings and other species. For example, knowledge about the environment is primarily manifested as an ability to classify a variety of situations from the point of view of survival in the real world : : : Classification on more abstract levels, seems to be a key issue in reasoning, learning and decision making, ...