计算机科学 ›› 2009, Vol. 36 ›› Issue (10): 213-216.

• 人工智能 • 上一篇    下一篇

一种区间数分解与定标算法及其扩展形式背景的概念格生成方法

刘耀华,周文,刘宗田   

  1. (上海大学计算机工程与科学学院 上海 200072)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金(60575035),上海高校选拔培养优秀青年教师科研专项基金(shu-07027,上海市重点学科建设项目(J50103}资助。

Interval Scaling Algorithm and its Concept Lattice Construction from Extended Formal Context

LIU Yao-hua, ZHOU Wen, LIU Zong-tian   

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

摘要: 现有的概念格模型无法处理既包含以布尔值表示的信息,又包含以标量、模糊数及区间数表示的信息。因此,针对包含所有这些信息类型的扩展的形式背景提出它的处理方法,在此基础上,生成经扩展的概念格,是一项有意义的工作。提出了一种新的区间数分解与定标算法,以处理含有多种类型的扩展形式背景,并给出了相应的扩展格生成算法。最后,实验表明,该方法具有良好的效果。

关键词: 概念格,概念格构造算法,形式概念分析,模糊概念格,区间概念格

Abstract: The existing concept lattice model is unable to process data which contains not only fuzzy information but also scalar and Boolean information. The extended formal context includes many kinds of information such as scalar,fuzzy, l3oolcan, and interval. Therefore, how to build concept lattice from extended formal context, is a meaningful study. An interval scaling algorithm is proposed here to deal with the extended formal context and a corresponding concept lattice construction method is produced. In the end of this paper, the experimental results show that this algorithm is useful.

Key words: Concept lattice, Construction algorithm, Formal concept analysis, Fuzzy concept lattice, Interval concept latticc

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