计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (8): 78-81.

• 学术探讨 • 上一篇    下一篇

基于权重联系度粗集理论的增量式知识发现

刘高峰,牟廉明

  

  1. 内江师范学院 数学系,四川 内江 641112
  • 收稿日期:2007-07-10 修回日期:2007-10-16 出版日期:2008-03-11 发布日期:2008-03-11
  • 通讯作者: 刘高峰

Incremental KDD of rough sets model based on weight connection degree

LIU Gao-feng,MOU Lian-ming   

  1. Mathematics Department,Neijiang Teacher School,Neijiang,Sichuan 641112,China
  • Received:2007-07-10 Revised:2007-10-16 Online:2008-03-11 Published:2008-03-11
  • Contact: LIU Gao-feng

摘要: 在实际生活中,信息系统的增量数据会不断产生,如何充分利用以前计算的结果结合新产生的数据进行新的知识发现,这是有意义的。针对这样的问题,提出了基于权得联系度的粗集模型,它着重考虑了条件属性重要性存在差异来建立粗集模型,而条件属性重要性可以通过以前数据的知识发现计算出来,于是利用基于权重联系度的粗集模型在对新产生的数据进行知识发现时,利用了以前的数据信息。建立了基于权重联系度的粗集模型及其对应的属性和属性值约简理论,最后通过一个示例来演示增量式知识发现的方法。

Abstract: In practices,the increasing data always come up.How to find useful information in increasing data is important and necessary.The rough sets model based on weight connection degree is produced.The attributes importance is different in the model,and the importance can be calculated from before data,so it is related to before data when the rough sets model based on weight connection degree is used to KDD.Then the reduction theory is produced,and an example is given to show the reduction’s algorithm.