Earticle

초록

영어

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relationships among different core sets and attribute reducts are further discussed. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets.

목차

Abstract
 1. Introduction
 2. Basic Notions
 3. The Relationships among Attribute Reduction Algorithms
  3.1. The Relationships among Five Core Sets
  3.2. The Relationships among Five Relative Reducts
 4. Efficient Attribute Reduction Algorithms Using Relative Discernibility Degree for Inconsistent Decision Tables
  4.1. Discernibility Object Pair and Relative Discernibility Degree
  4.2. The Relationships among Three Attribute Reduction Algorithms Using Dependency Function, Information Gain and Relative Discernibility Degree
  4.3. Efficient Attribute Reduction Algorithms from View of the Boundary Region
 5. Experimental Analysis
  5.1. Efficiency Evaluation
  5.2. Related Discussion
 6. Conclusions
 References

키워드

저자

• Yuhui Chang [ School of Computer Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China ]
• Jin Qian [ School of Computer Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, 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