Earticle

초록

영어

In this paper, we propose a Dual-Reduct method to generate core rules from original data sets for decision making. We rank the rules by rule usefulness after the step of first reduct. Then we take the useful rules as condition attribute and construct another new decision table. After the step of second reduct we generate core rules from the new constructed decision table. In our approach the generation process is straightforward and objective. At the same time, our approach can significantly reduce the number of rules comparing to the traditional generation approach because we adopt rule usefulness as a measure of core rules. We also provide theoretical proofs and deductions. Our approach is proved to be feasible and effective in a production security system.

목차

Abstract
 1. Introduction
 2. Dual-Reduct Method
  2.1. Motivation
  2.2. The Concept of Association Rules and Rule Usefulness Measure
 3. The Dual-Reduct Method to Generate Core Rules
  3.1. The Dual-Reduct Method Model
  3.2. The Method to Generate the Core Rules
  3.3. Complex Analyses
 4. Experiment of Prison Information Security and Prisoners’ Reform
  4.1 Example of Artifical Car Data Set
  4.2. Prison Computer Information Security Data Set
  4.3. Prisoners’ Reform Data Set
  4.4. Differences between Dual-Reduct Method and Traditional Reduct Approach
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Lv Hanfei [ Department of Information and Management Zhejiang Police Vocational Academy, Hangzhou, Zhejiang Province, China ]
• Jiang Congfeng [ School of Computer Science and Technology Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605