Earticle

초록

영어

The author conducts research on the mining problem of implication type data aiming at the characteristics of implication, uncertainty, nonlinearity, dynamic, complexity existing in the process of data mining and establishes an extension mining model of implication type data with multi factors based on extension theory. This model, first of all, carries out implication analysis on the implication type data and builds corresponding implication set; then, the author conducts extension classification on the hypogynous factor in the implication set and builds classical field and segment field of the epigynous factorin the implication set based on extension type divided; the author also respectively builds the correlation function and extension goodness-of-fit model between targeted mining object and classical field of epigynous factorin the implication set, acquires comprehensive extension goodness-of-fit considering the weight of epigynous factors, which, in other words, determines the degree of closeness between targeted mining object and extension type and thus achieves the mining of implication type data. Finally, the author demonstrates the feasibility of this model by explaining and verifying the venture capital case of an enterprise.

목차

Abstract
 1. Introduction
 2. Extension Data Mining Process Model in Complex System
  2.1. The Generation of Implication Set
  2.2. Matter Element Modeling
  2.3. Classical Field and Segment Field
  2.4. Implication Index Weight
  2.5. Extension Goodness-of-Fit
  2.6. The Realization of Data Mining Algorithm
 3. Application Analysis
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Xianmin Wei [ School of Computer Engineering, Weifang University, 5147 Eastern Dongfeng Street, Weifang, 261061, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605