In many real world applications, data are organized by coverings, not partitions. Covering-based rough sets have been proposed to cope with this type of data. Covering-based rough set theory is more general than rough set theory, then there is a need to employ sophisticated theories to make it more adaptive to real world application. The covering is one of core concepts in covering-based rough ...

To tackle the problem with inexact, uncertainty and vague knowl- edge, constructive method is utilized to formulate lower and upper approx- imation sets. Rough set model over dual-universes in fuzzy approximation space is constructed. In this paper, we introduce the concept of rough set over dual-universes in fuzzy approximation space by means of cut set. Then, we discuss properties of rough se...

Rough set theory provides a useful mathematical concept to draw tends to serve well for data mining applications whereas the predictive model. The rough set toolkit for analysis of data (ROSETTA), which is an advanced machine learning algorithms for data mining tasks implemented in Java (33). Therefore, this paper presents the RoughSets package that allows researchers. Zdzislaw Pawlak, Rough Se...

A method for indicator selection is proposed in this paper. The method, which adopts the General Methodology and Design Research approach, consists of four steps: Problem Identification, Requirement Gathering, Indicator Extraction, and Evaluation. Rough Set approach also has been applied in the Indicator Extraction phase. This phase consists of 5 steps: Data selection, Data Preprocessing, Discr...

Identity disclosure is one of the most serious privacy concerns in many data mining applications. A wellknown privacy model for protecting identity disclosure is k-anonymity. The main goal of anonymizing classification data is to protect individual privacy while maintaining the utility of the data in building classification models. In this paper, we present an approach based on rough sets for m...

In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special...

Attribute reduction plays an important role in the areas of rough sets and granular computing. Many kinds of attribute reducts have been defined in previous studies. However, most of them concentrate on data only, which result in the difficulties of choosing appropriate attribute reducts for specific applications. It would be ideal if we could combine properties of data and user preference in t...

Clustering algorithms gives general description of the clusters listing number of clusters and member entities in those clusters. It lacks in generating cluster description in the form of pattern. Deriving pattern from clusters along with grouping of data into clusters is important from data mining perspective. In the proposed approach reduct from rough set theory is employed to generate patter...

Humans can easily recognize complex objects even if values of their attributes are imprecise and often inconsistent. It is not clear how the brain processes uncertain visual information. We have tested electrophysiological activity of the visual cortex (area V4), which is responsible for shape classifications. We formulate a theory in which different visual stimuli are described through their a...

Rough se theory(RST) has two formats, abstract and table formats. In this article, abstract format is hardly touched. The table format, by definition, is a theory of extensional relational databases. However, their fundamental goals are very different. RST has focused on data analysis and mining, while database has focused on data processing. In RST, a relation, which is also known as informati...