NMGRS: Neighborhood-based multigranulation rough sets

AbstractRecently, a multigranulation rough set (MGRS) has become a new direction in rough set theory, which is based on multiple binary relations on the universe. However, it is worth noticing that the original MGRS can not be used to discover knowledge from information systems with various domains of attributes. In order to extend the theory of MGRS, the objective of this study is to develop a so-called neighborhood-based multigranulation rough set (NMGRS) in the framework of multigranulation rough sets. Furthermore, by using two different approximating strategies, i.e., seeking common reserving difference and seeking common rejecting difference, we first present optimistic and pessimistic 1-type neighborhood-based multigranulation rough sets and optimistic and pessimistic 2-type neighborhood-based multigranulation rough sets, respectively. Through analyzing several important properties of neighborhood-based multigranulation rough sets, we find that the new rough sets degenerate to the original MGRS when the size of neighborhood equals zero. To obtain covering reducts under neighborhood-based multigranulation rough sets, we then propose a new definition of covering reduct to describe the smallest attribute subset that preserves the consistency of the neighborhood decision system, which can be calculated by Chen’s discernibility matrix approach. These results show that the proposed NMGRS largely extends the theory and application of classical MGRS in the context of multiple granulations

Examining Granular Computing from a Modeling Perspective

In this paper, we use a set of unified components to conduct granular modeling for problem solving paradigms in several fields of computing. Each identified component may represent a potential research direction in the field of granular computing. A granular computing model for information analysis is proposed. The model may suggest that granular computing is an instrument for implementing perception based computing based on numeric computing. In addition, a novel granular language modeling technique is proposed for information extraction from web pages. This paper also suggests that the study of data mining in the framework of granular computing may address the issues of interpretability and usage of discovered patterns

Information Flow Model for Commercial Security

Information flow in Discretionary Access Control (DAC) is a well-known difficult problem. This paper formalizes the fundamental concepts and establishes a theory of information flow security. A DAC system is information flow secure (IFS), if any data never flows into the hands of owner’s enemies (explicitly denial access list.