원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
vol.3 no.1
2010.03
pp.33-52
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
참고문헌
자료제공 : 네이버학술정보