The concept of classifying the records of the information system has been due to Two Way Approach [ ie, lower and upper approximations ] of Pawlak’s rough sets model. But the approximation does to take into consideration the degree of contribution of the basic categories. This deficiency was eliminated in early nineties by Ziarko who has proposed VPRS model and later on various efforts were mad...

Some properties of Intuitionistic Fuzzy (IF) rough relational algebraic operators under an IF rough relational data model are investigated and illustrated using diabetes and heart disease databases. These properties are important and desirable for processing queries in an effective and efficient manner. Keywords— IF Set, Rough Set, IF Rough Relational Database, IF rough Relational Operators.

Article history: Received 4 May 2014 Received in revised form 9 December 2014 Accepted 12 December 2014 Available online 26 December 2014

Since data increases with time and space, many incremental rough based reduction techniques have been proposed. In these techniques, some focus on knowledge representation on the increasing data, some focus on inducing rules from the increasing data. Whereas there is less work on incremental feature selection (i.e., attribute reduction) from the increasing data, especially the increasing real v...

In this paper, within the context of fuzzy rough set theory, we generalize the classical rough set framework for data-based attribute selection and reduction, based on the notion of fuzzy decision reducts. Experimental analysis confirms the potential of the approach.

This paper explores the implications of approximating a concept based on the Bayesian decision procedure, which provides a plausible unification of the fuzzy set and rough set approaches for approximating a concept. We show that if a given concept is approximated by one set, the same result given by the α-cut in the fuzzy set theory is obtained. On the other hand, if a given concept is approxim...

Purpose – The purpose of this paper is to develop a novel two-stage model for promoting the effect of rule generation based on rough set. In order to improve traditional rough set method, the novel two-stage model adopts new kernel intuitionistic fuzzy clustering (KIFCM) to promote performance of rough set theory. Moreover, the e-learning customer data set in Taiwan is also examined for demonst...

This paper presents some highlights in the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition. These techniques are capable of dealing with inexact and imprecise problem domains and have been demonstrated to be useful in the solution of classification problems. It addresses the issue of the application of appropriate evaluation cri...

Ever since the first hybrid fuzzy rough set model was proposed in the early 1990’s, many researchers have focused on the definition of the lower and upper approximation of a fuzzy set by means of a fuzzy relation. In this paper, we review those proposals which generalize the logical connectives and quantifiers present in the rough set approximations by means of corresponding fuzzy logic operati...

Granular computing is a computational paradigm in which a granule represents a structure of patterns evolved by performing operations on the individual patterns. Two granular neural networks are described for performing the pattern analysis tasks like classification and clustering. The granular neural networks are designed by integrating fuzzy sets and fuzzy rough sets with artificial neural ne...