东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2008, Vol. 29 ›› Issue (1): 21-24.DOI: -

• 论著 • 上一篇    下一篇

基于粗糙集理论和层次分析的数据约简

张雪峰;田晓东;张庆灵;   

  1. 东北大学理学院;东北大学理学院;东北大学理学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2008-01-15 发布日期:2013-06-22
  • 通讯作者: Zhang, X.-F.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60574011)

Data reduction based on rough set theory and hierarchic analysis

Zhang, Xue-Feng (1); Tian, Xiao-Dong (1); Zhang, Qing-Ling (1)   

  1. (1) School of Sciences, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2008-01-15 Published:2013-06-22
  • Contact: Zhang, X.-F.
  • About author:-
  • Supported by:
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摘要： 为更有效更迅速地获得大量信息中所包含的潜在知识和规律,有关数据挖掘和数据库知识发现的研究变得更为广泛和深入.结合粗糙集理论的优势和层次分析模型的特点并将两者有机地结合起来,通过在非核属性中引入重要性概念并利用简单相异矩阵,提出了基于粗糙集理论和层次分析的数据约简算法,同时证明了该算法的有效性和完备性.最后,应用该算法解决了医疗决策系统中一个数据约简问题,实现了知识和规律的挖掘,提高了数据约简的合理性.

关键词: 粗糙集理论, 数据约简, 简单相异矩阵, 层次分析法, 重要性

Abstract: To find latent knowledge rules efficiently and quickly from a great deal of information, the studies on data mining (DM) and knowledge discovery of database (KDD) become more popular and profound. Combining the rough set theory with hierarchic analysis model in view of their characteristics and introducing the definition of importance into non-core attributes, a new data reduction algorithm is proposed using a simple discriminative matrix. The validity and completeness of the algorithm is proved, by which an ideographic problem is common in medical treatment decision-making system is successfully solved. The results reveal that the algorithm is able to mine the knowledge and rules effectively and improve the rationality of data reduction.

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