Earticle

초록

영어

This paper has been explored information data prediction implementation access based on data mining combination model. With data mining technology as the entry point and in combination with the analysis on information data prediction characteristics. Research on variable substitution to non-linear regression forecast model precision's influence, and seek the modeling method that can improve the forecast precision. Based on the Data mining, the transform in space and the weighted processing combined method, make full use of information that the primary data provide. Given modeling method of combination forecast model based on the Data mining. Based on Data mining’s combination forecast model’s modeling method can reduce the serious influence that the variable substitution brings and has fully used useful information in the primary data. It obviously improved the accuracy of the prediction model.

목차

Abstract
 1. Introduction
 2. Data Clustering, Classification and Feature Selection Algorithm
  Efficient Rather than Accurate
  Relevancy Rather than Causality
  Information Data Prediction Access Based on Data Mining Combination Model
  Form of the Nonlinear Mathematical Model
  Method to Approximate Common Nonlinear Mathematical Models to Regression Model Parameters
  Improvement of Approximating Common Nonlinear Mathematical Models to Regression Model Parameters
  Method of the Combination Information Forecasting Model Based on Data Mining
  In Empirical Analysis
 Conclusion
 References

키워드

저자

• Yiran Wang [ College of Network Engineering, Zhoukou Normal University, henan, ZhouKou, China ]
• Guang Zheng [ College of Network Engineering, Zhoukou Normal University, henan, ZhouKou, 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