Earticle

초록

영어

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we focus our discussion on the rough set theory for categorical data clustering. We propose MADE (Maximal Attributes DEpendency), an alternative technique for categorical data clustering using rough set theory taking into account maximum attributes dependencies degree in categorical-valued information systems. Experimental results on two benchmark UCI datasets show that MADE technique is better with the baseline categorical data clustering technique with respect to computational complexity and clusters purity.

목차

Abstract
 1. Introduction
 2. Rough Set Theory
 3. TR and MMR Techniques
  3.1. The TR Technique
  3.2. The MMR Technique
  3.3. Comparison of TR and MMR techniques
 4. Maximum Attributes DEpendencies (MADE) Technique
  4.1. MADE technique
  4.2. Complexity
  4.3. Example
  4.4. Objects splitting
 5. Comparison Tests
  5.1. Soybean dataset
  5.2. Zoo dataset
  5.3. Comparison
 6. Conclusion
 References

키워드

저자

• Tutut Herawan [ Department of Mathematics Education Universitas Ahmad Dahlan, Yogyakarta, Indonesia ]
• Rozaida Ghazali [ Faculty of Information Technology and Multimedia Universiti Tun Hussein Onn Malaysia, Johor, Malaysia ]
• Iwan Tri Riyadi Yanto [ Department of Mathematics Universitas Ahmad Dahlan, Yogyakarta, Indonesia ]
• Mustafa Mat Deris [ Faculty of Information Technology and Multimedia Universiti Tun Hussein Onn Malaysia, Johor, Malaysia ]

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