원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.9 No.1
2014.01
pp.219-230
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Computational models of the artificial intelligence such as rough set theory have several applications. Rough set-based data clustering can be considered further as a technique for medical decision making. This paper presents the results of an experimental study of a rough-set based clustering technique using Variable Precision Rough Set (VPRS). Here, we employ our proposed clustering technique [12] through a medical dataset of patients suspected diabetic. Our results indicate that the VPRS-based technique is better than that the standard rough set-based techniques in the process of selecting a clustering attribute.
목차
Abstract
1. Introduction
2. Variable Precision Rough Set
2.1. Set Approximations
2.2. Variable Precision Rough Set
3. Rough Set-based Techniques for Selecting a Clustering Attribute
4. Experiment Results
4.1. Material
4.2. Clustering problem
4.3. Result
4.4. Cluster purity and its visualization
5. Conclusion
Acknowledgements
References
1. Introduction
2. Variable Precision Rough Set
2.1. Set Approximations
2.2. Variable Precision Rough Set
3. Rough Set-based Techniques for Selecting a Clustering Attribute
4. Experiment Results
4.1. Material
4.2. Clustering problem
4.3. Result
4.4. Cluster purity and its visualization
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보