formal concept analysis,concept lattice; concept generation,search space,data mining ,"/> 基于增量更新内涵的形式概念生成算法

吉林大学学报(信息科学版) ›› 2022, Vol. 40 ›› Issue (3): 452-463.

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基于增量更新内涵的形式概念生成算法

吴清寿1a,1b,2 , 郭 磊1a,1b , 余文森1a,1b   

  1. 1. 武夷学院 a. 数学与计算机学院; b. 认知计算与智能信息处理福建省高校重点实验室, 福建 武夷山 354300; 2. 福建农林大学 智慧农林福建省高校重点实验室, 福州 350002
  • 收稿日期:2021-11-07 出版日期:2022-07-14 发布日期:2022-07-15
  • 作者简介:吴清寿(1977— ), 男, 福建莆田人, 武夷学院教授, 主要从事复杂网络、 形式概念分析和机器学习研究, ( Tel) 86- 13860073026(E-mail)wuqsh@ qq. com。
  • 基金资助:
    国家社会科学基金资助项目 (19XGL010 ); 福建省自然科学基金资助项目 ( 2019J01835; 2019J01834; 2020J01420; 2021J011144); 智慧农林福建省高校重点实验室开放课题基金资助项目(2019LSAF03)

Formal Concept Generation Algorithm Based on Incremental Update Intent

WU Qingshou1a,1b,2 , GUO Lei1a,1b , YU Wensen1a,1b   

  1. 1a. College of Mathematics and Computer Science; 1b. The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions, Wuyi University, Wuyishan 354300, China; 2. Key Laboratory of Smart Agriculture and Forestry Fujian Province University, Fujian Agriculture and Forestry University, Fuzhou 350002, China
  • Received:2021-11-07 Online:2022-07-14 Published:2022-07-15

摘要: 为高效率地生成概念, 提出一种基于增量更新内涵的概念生成算法 IUICG( Incremental Updating Intension based Concepts Generation)。 首先将背景中的属性逐个设定为任务属性, 以任务属性将概念搜索空间划分为先行概念集合和新增概念集合, 提高了搜索效率。 其次提出了概念操作规则, 其中外延过滤规则避免了无效外延对概念空间的搜索, 内涵更新规则和新增概念规则提升了概念生成速度。 实验结果表明, 在不同类型的数据集上, IUICG算法的时间性能都优于对比算法。 在对象数量远大于属性数量的数据集上, IUICG 算法具有接近线性的时间复杂度。

关键词: 形式概念分析, 概念格, 概念生成, 搜索空间, 数据挖掘

Abstract: In order to generate concepts efficiently, a concept generation algorithm IUICG(Incremental Updating Intent Based Concepts Generation) is proposed. First, the attributes in the formal context are set as task attributes one by one, and the concept search space is divided into privious concept set and newly-add concept set by the task attributes, which improves the search efficiency. Secondly, the concept operation rules are proposed, in which the extent filtering rules avoid the search of concept space by invalid extent, and the intent update rules and newly-add concept rules improve the concept generation speed. The experimental results show that the time performance of the IUICG algorithm is better than that of comparison algorithm on different types of data sets. The IUICG algorithm has nearly linear time complexity on data sets where the number of objects is much greater than the number of attributes. 

Key words: formal concept analysis')">

formal concept analysis, concept lattice; concept generation, search space, data mining

中图分类号: 

  • TP18