计算机科学 ›› 2013, Vol. 40 ›› Issue (4): 214-216.

• 人工智能 • 上一篇    下一篇

粗糙概念格分层建格算法及应用

刘保相,陈焕焕,柳洁冰   

  1. 河北联合大学理学院唐山063009;河北联合大学理学院唐山063009;河北联合大学理学院唐山063009
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受河北省自然科学基金(A2011209046,A2012209030)资助

Layered Construction Algorithm and Application of Rough Concept Lattice

LIU Bao-xiang,CHEN Huan-huan and LIU Jie-bing   

  • Online:2018-11-16 Published:2018-11-16

摘要: 粗糙概念格能够反映对象与特征间的确定与不确定关系,具有处理不确定性知识的能力,格的构建在应用过程中具有重要的意义。通过分析粗糙概念格的概念和结构,并结合一般概念格的构建思想,针对决策形式背景,提出一种以决策属性值为切入点的粗糙概念格的分层建格方法,从而丰富了粗糙概念格的构建理论。通过实例验证了该方法简单直观,效果良好。

关键词: 粗糙概念格,分层建格,决策属性值,近似,内涵,外延

Abstract: Rough concept lattice can reflect the certain and uncertain relationships between objects and attributes,and can deal with uncertainty knowledge.It is an important task to construct concept lattice efficiently in the applications.Analyzing the concept and structure of rough concept lattice,and combining with the construction of the general concept lattice thought,this paper proposed a layered construction algorithm,which is based on the decision-attribute value in decision context as the breakthrough point,and enriches the construction theory.The examples show that this algorithm is simple and intuitive,and gets good effect.

Key words: Rough concept lattice,Layered construction algorithm,Decision-attribute value,Approximation,Intent,Extent

[1] Wille R.Restructuring lattice theory:An approach based on hie-rarchies of concepts[C]∥Rival I,ed.Ordered Sets.Reidel,1982:415-470
[2] 胡可云,陆玉昌,石纯一.概念格及其应用进展[J].清华大学学报:自然科学版,2000,40(9):77-81
[3] 杨海峰,张继福.一种新的概念格结构:粗糙概念格[J].计算机技术与应用进展,2006:212-216
[4] 沈夏炯,韩道军,刘宗田,等.概念格构造算法的改进[J].计算机工程与应用,2004,4:100-103
[5] Xie Zhi-peng,Liu Zong-tian.A Fast Incremental Algorithm for Building Concept Lattice[J].Chinese J.Computers,2002,5:490-496
[6] Godin R.Incremental concept formation algorithm based on Galois (concept) lattices[J].Computers Intelligence,1995,11(2):246-267
[7] 杨海峰,张继福.粗糙概念格及构造算法[J].计算机工程与应用,2007,43(24):172-175
[8] 黄加增.基于粗糙概念格的多决策属性分析[D].昆明:昆明理工大学,2008

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!