Earticle

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

키워드

저자

• Yanrong Zhang [ College of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China ]
• Zhijie Zhao [ College of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China ]
• Jing Yu [ Heilongjiang science and technology museum, Harbin 150018, China ]
• Kun Wang [ College of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605