Earticle

초록

영어

Mining association rules for data is not only an essential part of data mining but also a hot issue in knowledge engineering and researches on data mining technology. Since multi-data mining is characterized as being multi-type, multi-level, multi-implicational and complicated, the efficiency of multi- data association rule mining usually cannot be high, precision and accuracy are of a relatively low degree, and the targets of mining cannot be obtained quickly. Therefore, on the basis of improving traditional association rule mining algorithm, this paper researched on multi-data association rule mining algorithm and based on grey relational analysis, proposed a multi-data association rule mining algorithm. Firstly, the associate objects most relevant to the target objects are obtained through the grey relational analysis, which helps to form single- or multi-target data associates; after that, the multi-data association rule mining model which sets data associates as the new mining objects is established. Under the conditions that the level of support and confidence are met, the frequent patterns of corresponding data associates and further, the multi-data association rule, are obtained. Simulation experiments implied that the model have the advantages of simplicity, practicality, operability, decent precision and accuracy.

목차

Abstract
 1. Introduction
 2. The Obtainment of Data Associates Based on Grey Relational Analysis
  2.1. The Generation of Single-Target Data Associates
  2.2. The Generation of Multi-Target Data Associates
 3. The Mining Association Rules for Data Based on the Substrate ofFrequent Patterns
  3.1. The Basic Definitions of Substrate of Frequent Patterns
  3.2. The Improved Multi-Data Association Rule Mining Algorithm
  3.3. Realization of the Multi-Data Association Rule Mining Model Based on Grey Relational Analysis
 4. Verification and Analysis of Algorithm
 5. Conclusions
 References

저자

• Hongyan Chu [ Nanjing College of Information Technology, Nanjing, Jiangsu, 210023 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