Inconsistent Neighborhoods and Relevant Properties in Neighborhood Rough Set Models

Abstract

Abstract-Rough set theory is an important branch of data mining and machine learning, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, and explore the relations between it and the existing notions in neighborhood rough set models. Some interesting properties are obtained accordingly. These properties can generate some new solutions to compute the quantities in neighborhood rough set, which are often more direct and more quick to obtain the results than the previous solutions.