원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
vol.4 no.1
2011.03
pp.7-18
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper focuses on generalization of rough set model and rule induction. First a extension of rough set approximations is established on general granular structure, so that the rough set models on some special granular structures are meaningful. The new rough approximation operators are interpreted by topological terminology well. Conversely, by means of the new rough approximation operators, many special granular structures, such as, covering, knowledge space, topology space and Pawlak approximation space, are characterized. Furthermore, using new approximation operators, two types of decision rules can be induced.
목차
Abstract
1. Introduction
2. Preliminaries
2.1. Set systems and set operators
2.2. Coverings
2.3. Closure systems and closure operators
2.4. Topologies and interior operators
2.5. Partitions
3. Rough set model based on granular structure
3.1 Pawlak rough sets
3.2 Rough set approximations on granular structure
4 Approximation operator characterizations of granular structures
5 Rule induction
6 Conclusions
References
1. Introduction
2. Preliminaries
2.1. Set systems and set operators
2.2. Coverings
2.3. Closure systems and closure operators
2.4. Topologies and interior operators
2.5. Partitions
3. Rough set model based on granular structure
3.1 Pawlak rough sets
3.2 Rough set approximations on granular structure
4 Approximation operator characterizations of granular structures
5 Rule induction
6 Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보