원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.8 No.4
2015.04
pp.69-76
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
To solve the traditional problem of knowledge acquisition bottleneck in e-commerce, an improved algorithm of attribute reduction based on discernibility matrix is proposed. The algorithm is used to attribute reduction for e-commerce consumer behavior prediction. With rule extraction model of rough sets, the rules of e-commerce consumer behavior prediction are acquired. Practical example of consumer behavior prediction shows that the proposed approach can be handled found knowledge effectively and can be converted the available rules easily. It has strong ability of fault tolerance and can improve the speed and quality of knowledge acquisition. The method has good practical value.
목차
Abstract
1. Introduction
2. Application of Rough Sets
2.1 Lower Approximation and the Approximation
2.2 Knowledge Reduction
2.3 Knowledge Representation System
2.4 Decision Tables
2.5 Distinguish and Differentiate Function Matrix
3. Predict Consumer Behavior based on Rough Set of E-commerce
3.1 Data Preprocessing
3.2 Reduction Condition Attributes Set
3.3 Extraction and Reduction Rules
4. Conclusion
Acknowledgements
References
1. Introduction
2. Application of Rough Sets
2.1 Lower Approximation and the Approximation
2.2 Knowledge Reduction
2.3 Knowledge Representation System
2.4 Decision Tables
2.5 Distinguish and Differentiate Function Matrix
3. Predict Consumer Behavior based on Rough Set of E-commerce
3.1 Data Preprocessing
3.2 Reduction Condition Attributes Set
3.3 Extraction and Reduction Rules
4. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보