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Rough Set Approach for Categorical Data Clustering

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

저자정보

  • 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

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