Journal of South China University of Technology (Natural Science Edition) ›› 2006, Vol. 34 ›› Issue (9): 50-55.

• Computer Science & Technology • Previous Articles     Next Articles

Attribute Reduction Using Attribute Variable Partition of Rough Set

Deng Jiu-ying1  Mao Zong-yuan1  Xu Ning2   

  1. 1.School of Automation Science and Engineering,South China Univ.of Tech.,Guangzhou 510640,Guangdong,China2. Dept. of Computer Science.Guangdong Institution of Education,Guangzhou 510303,Guangdong.China
  • Received:2005-12-05 Online:2006-09-25 Published:2006-09-25
  • Contact: 邓九英(1962-)，女，在职博士生，广东教育学院副教授，主要从事智能控制、数据挖掘与仿真技术方面的研究 E-mail:djyl111@126.com
  • About author:邓九英(1962-)，女，在职博士生，广东教育学院副教授，主要从事智能控制、数据挖掘与仿真技术方面的研究

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Abstract:

By means of the methodology of rough set,different attribute classification methods for decision systems are first analyzed in this paper,and the attribute significance as well as the extremely small subset of the attribute relative reduction caused by a classification variety is discussed.Next, the intrinsic factor to affect the interaction between the attribute classification method and the attribute reduction results is revealed. Then.a scheme for performing effective attribute classification and a strategy for determining reasonable attribute reduction subsets are in-troduced,and an algorithm is presented to implement the corresponding software according to the strategy . By using the presented algorithm,a relative reduction subset is finally selected. Experimental results verify the effectiveness of the proposed strategy and algorithm.

Key words: attribute-value partition, relative reduction, extremely small subset, attribute significance, core attribute