원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.4 No.3
2011.09
pp.1-12
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The theory of soft set proposed by Molodtsov in 1999 is a new method for handling uncertain data and can be redefined as a Boolean-valued information system. The soft set theory has been applied to data analysis and decision support systems based on large data sets. Using retrieved datasets, we will be comparing two techniques in solving incomplete datasets : parity bits of supported set; and the aggregate and calculated support values. It is demonstrated in this paper that the technique using aggregate values and calculated support values performs better in the process of identifying missing values in incomplete datasets.
목차
Abstract
1. Introduction
2. Preliminaries
2.1 Information System
2.2 Soft Set Theory
2.3 Supported Sets
2.4 Parity Bits
2.5 Attribute Aggregate Values
2.6 Diagonal Aggregate Values
3. Analysis of Techniques
3.1. Analysis of attribute reduction of soft set in Mohd Rose A. N. et al. [8]
3.2. Analysis of attribute reduction of soft set in Mohd Rose A. N. et al. [9]
4. Conclusion
References
1. Introduction
2. Preliminaries
2.1 Information System
2.2 Soft Set Theory
2.3 Supported Sets
2.4 Parity Bits
2.5 Attribute Aggregate Values
2.6 Diagonal Aggregate Values
3. Analysis of Techniques
3.1. Analysis of attribute reduction of soft set in Mohd Rose A. N. et al. [8]
3.2. Analysis of attribute reduction of soft set in Mohd Rose A. N. et al. [9]
4. Conclusion
References
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