Earticle

초록

영어

There are fewer techniques to group objects having similar characteristics deal with categorical data ,but some are of them be complicated in the clustering process while others have stability issues. In this paper we represent a new technique which it be more easier than the other techniques in computing the selecting clustering attribute process and at the same time having stability issues besides taking care of handling uncertainty and categorical data together, we called it (maximum significance of attributes) MSA. The proposed technique based on rough set theory by taking into account the concept of significance of attributes of the database. We analyzing and comparing the performance of MSA technique with (bi-clustering) BC, (total roughness) TR, (minimum-minimum roughness) MMR and (maximum dependency of attribute) MDA techniques.

목차

Abstract
 1. Introduction
 2. The Main Concepts of Important Definitions
 3. Proposed Algorithm
 4. Experimental Part
  4.1. Computational Part
 5. The performance comparisons of MSA with that of BC, TR, MMR and MDA techniques
  5.1. Objects splitting for TR, MMR and MDA techniques
  5.2. The Purity Ratio for TR, MMR and MDA Techniques
  5.3. Objects Splitting for MSA Technique
  5.4. The Purity Ratio for MSA Technique
 6. Conclusions
 Acknowledgement
 References

키워드

저자

• W. A. Hassanein [ Mathematics Department Faculty of science, Tanta University, Tanta, Egypt ]
• Amr A. Elmelegy [ Mathematics Department Faculty of science, Tanta University, Tanta, Egypt ] Corresponding author

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