期刊文献+

基于三支概念格线图的混合蕴含获取 被引量:5

Mining Mixed Implications Based on the Line Diagrams of Three-way Concept Lattices
下载PDF
导出
摘要 考虑到三支概念分析中三支算子可以表达对象集和属性集之间"共同具有"和"共同不具有"这两种语义,基于该理论对混合蕴含规则进行研究.首先定义三支概念分析下的混合蕴含规则,并利用三支算子的性质给出混合蕴含规则成立的充分必要条件;然后根据三支概念的构建算法以及三支概念之间的偏序关系给出三支概念格线图的构建方法;最后,基于三支概念格线图提出混合蕴含规则的获取方法. In the theory of three-way concept analysis, three-way operator expressed the semantics of "jointly possessed" and " jointly not possessed" between the objects and the attributes of a formal con-text. On the basis of such viewpoints, mixed implications were studied. Mixed implications in three-way concept analysis were defined f irs t ly, and the necessary and sufficient conditions to make the mixed impli-cations hold were given. Then the method of constructing the line diagrams of three-way concept lattices was given based on the algorithms for constructing three-way concepts and the partial order relations be-tween three-way concepts. Finally, the approach to mining mixed implications from the line diagrams of three-way concept lattices was proposed.
作者 朱晓敏 祁建军 ZHU Xiaomin;QI Jianjun(School of Computer Science and Technology, Xidian University, Xi' an 710071, China)
出处 《郑州大学学报(理学版)》 CAS 北大核心 2017年第4期16-21,共6页 Journal of Zhengzhou University:Natural Science Edition
基金 国家自然科学基金项目(11371014) 陕西省自然科学基础研究计划项目(2014JM8306)
关键词 三支概念分析 三支算子 混合蕴含 三支概念格 线图 three-way concept analysis three-way operator mixed implication three-way concept lat-tice line diagram
  • 相关文献

参考文献6

二级参考文献40

  • 1张文修,魏玲,祁建军.概念格的属性约简理论与方法[J].中国科学(E辑),2005,35(6):628-639. 被引量:193
  • 2黄健斌,姬红兵.基于模糊概念格的Web搜索结果聚类算法[J].西安电子科技大学学报,2005,32(6):856-860. 被引量:6
  • 3ZHANG Wenxiu,WEI Ling,QI Jianjun.Attribute reduction theory and approach to concept lattice[J].Science in China(Series F),2005,48(6):713-726. 被引量:70
  • 4王德兴,胡学钢,刘晓平.一种新颖的基于量化概念格的属性归纳算法[J].西安交通大学学报,2007,41(2):176-179. 被引量:2
  • 5郭崇慧.数据挖掘教程[M].北京:清华大学出版社,2005.
  • 6HALKIDI M. On clustering validation techniques[J]. Journal of Information Systems, 2001,17(2,3) :107-145.
  • 7MASSEY L. Evaluating quality of text clustering with ART1 [ C ]// Proc of International Joint Conference on Neural Networks. 2003:20-24.
  • 8STEINBACH M, KARYPIS G, KUMAR V. A comparison of document clustering technique [ C ]//GROBELNIK M. Proc of Workshop on Text Mining. Boston : [ s. n. ], 2000 : 119-110.
  • 9SEBASTIANI F. Machine learning in automated text categorization [ J ]. ACM Computing Surveys, 2002,34 ( 1 ) : 1-47.
  • 10WU Bin, ZHENG Yi, LIU Shao-hui, et al. CSIM: a document clustering algorithm based on swarm intelligence evolutionary computation [ C]//Proc of World Congress on Computational Intelligence. Honolulu : IEEE Press, 2002:477-482.

共引文献61

同被引文献48

引证文献5

二级引证文献12

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部