计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 131-135.doi: 10.11896/jsjkx.200800018

• 数据库&大数据&数据科学 • 上一篇    下一篇

形式背景中概念及约简的动态更新方法

曾惠坤, 米据生, 李仲玲   

  1. 河北师范大学数学科学学院 石家庄 050024
  • 收稿日期:2020-08-03 修回日期:2020-09-27 出版日期:2021-01-15 发布日期:2021-01-15
  • 通讯作者: 米据生(mijsh@263.net)
  • 作者简介:835676297@qq.com
  • 基金资助:
    国家自然科学基金项目(61573127,61502144);河北省自然科学基金(F2018205196,F2019205295);河北省高等学校自然科学基金项目(BJ2019014);河北省博士后择优资助科研项目(B2016003013);河北省三三三人才杠程培养经费(A2017002112);河北省研究生创新资助项目(CXZZBS2020076)

Dynamic Updating Method of Concepts and Reduction in Formal Context

ZENG Hui-kun, MI Ju-sheng, LI Zhong-ling   

  1. College of Mathematical Sciences,Hebei Normal University,Shijiazhuang 050024,China
  • Received:2020-08-03 Revised:2020-09-27 Online:2021-01-15 Published:2021-01-15
  • About author:ZENG Hui-kun,born in 1995,master.Her main research interests include concept lattice,granular computing and so on.
    MI Ju-sheng,born in 1966,Ph.D,second professor,Ph.D supervisor.His main research interests include rough set,concept lattice,granular computing,approximate reasoning and so on.
  • Supported by:
    National Natural Science Foundation of China(61573127,61502144),Natural Science Foundation of Hebei Pro-vince(F2018205196,F2019205295),Natural Science Foundation of Higher Education Institutions of Hebei Province (BJ2019014),Postdoctoral Advanced Programs of Hebei Province(B2016003013),Training Funds for 333 Talents Project in Hebei Province(A2017002112) and Postgraduate Innovation Funding Project of Hebei Province(CXZZBS2020076).

摘要: 概念格作为一种知识结构被广泛应用于现实生活中的许多方面,当数据为动态时,形式概念的更新是不可避免的,概念的更新既是知识的补充也是信息的融合。 文中主要研究了在形式背景中添加单个属性或多个属性时概念的更新方法,讨论了属性约简和图的极小顶点覆盖在增添属性之后的变化情况;探讨了将动态添加属性放入决策形式背景中时,非冗余规则的提取和优化问题;在保持规则前件不变的条件下,研究了动态增加决策属性时,非冗余规则是如何变化的。

关键词: 概念更新, 规则提取, 属性约简

Abstract: Concept lattice is widely used as a knowledge structure in many real-life applications,and the updating of a formal concept is inevitable in dynamic cases.The updating of concepts is not only the supplement of knowledge but also the fusion of information.This paper mainly studies the method of concept updating when a single attribute or a subset of attributes is added into the formalcontext.The changes of reduction and the minimum vertex covering are discussed.Finally,the redundancy rules extraction and optimization problems are discussed when dynamic attribute is added into a decision formal context.Under the condition of keeping the antecedents of rules,the changes of non-redundant rules are studied when a decision attribute is added dynamically.

Key words: Attribute reduction, Concept update, Rule extraction

中图分类号: 

  • TP18
[1] GANTER B,WILLE R.Formal concept analysis:mathematical foundations[M].Berlin,Heidelberg:Springer,1999.
[2] BANERJEE M,MITRA S,PAL S K.Rough fuzzy MLP:Knowledge encoding and classification[J].IEEE Transactions on Neural Networks,1998,9:1203-1216.
[3] WEI W,LIANG J Y.Information fusion in Rough set theory:An overview[J].Information Fusion,2019,48:107-118.
[4] WONG S K M,ZIARKO W.Optimal decision rules in decision table[J].Bulletin of Polish Academy ofSciences,1985,33(11/12):693-696.
[5] WANG J,WANG J.Reduction algorithms based on discernibility matrix:The ordered attributes method[J].Journal of Computer Science and Technology,2001,16:489-504.
[6] QIN K Y,LIN H, JIANG Y T.Local attribute reductions of formal contexts[J].International Journal of Machine Learning and Cybernetics,2020,11(1):81-93.
[7] WILLE R.Restructuring lattice theory:an approach based on hierarchies of concepts[M]//Ordered Sets.Berlin Heidelberg:Springer,1982:445-470.
[8] ZHANG W X,WEI L,QI J J.Attribute reduction theory andMethod of concept lattice[J].Science in China (Series E:Information Science),2005,35(6):628-639.
[9] YAO Y Y.Concept lattices in rough set theory[C]//Fuzzy Information,2004.
[10] WEI L,QI J J,ZHANG W X.Reduction of concept lattice attribute of decision form context[J].Science in China (Series E:Information Science),2008(2):195-208.
[11] WEI L,QI J J,ZHANG W X.Study on the relationship between concept lattice and rough set[J].Computer Science,2006(3):18-21.
[12] LI J H,WU W Z.Granular computing method of formal concept analysis and its research prospect[J].Journal of Shandong University (Science edition),2017,52(7):1-12.
[13] ZHANG W X,LIANG Y,WU W Z.Information systems and knowledge discovery[M].Beijing:Science Press,2003.
[14] CHEN J K,MI J S,XIE B,et al.A fast attribute reduction method for large formal decision contexts[J].International Journal of Approximate Reasoning,2019,106:1-17.
[15] SHAO M W,WU W Z,WANG X Z,et al.Knowledge reduction methods of covering approximate spaces based on concept lattice[J].Knowledge-Based Systems,2020,191:105-269.
[16] MI J S,CHEN J K.Attribute reduction method of rough setbased on graph[J].Journal of Northwestern University (Natural Science),2019,49(4):508-516.
[17] BONDY J A,MURTY U S R.Graph Theory with Applications[M].London:Macmillan,1976.
[18] WEI L,LIU L,QI J J,et al.Rules acquisition of formal decision contexts based on three-way concept lattices[J].Information Sciences,2020,516:529-544.
[1] 王子茵, 李磊军, 米据生, 李美争, 解滨.
基于误分代价的变精度模糊粗糙集属性约简
Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost
计算机科学, 2022, 49(4): 161-167. https://doi.org/10.11896/jsjkx.210500211
[2] 王志成, 高灿, 邢金明.
一种基于正域的三支近似约简
Three-way Approximate Reduction Based on Positive Region
计算机科学, 2022, 49(4): 168-173. https://doi.org/10.11896/jsjkx.210500067
[3] 李艳, 范斌, 郭劼, 林梓源, 赵曌.
基于k-原型聚类和粗糙集的属性约简方法
Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets
计算机科学, 2021, 48(6A): 342-348. https://doi.org/10.11896/jsjkx.201000053
[4] 温馨, 闫心怡, 陈泽华.
基于等价关系的最小乐观概念格生成算法
Minimal Optimistic Concept Generation Algorithm Based on Equivalent Relations
计算机科学, 2021, 48(3): 163-167. https://doi.org/10.11896/jsjkx.200100046
[5] 桑彬彬, 杨留中, 陈红梅, 王生武.
优势关系粗糙集增量属性约简算法
Incremental Attribute Reduction Algorithm in Dominance-based Rough Set
计算机科学, 2020, 47(8): 137-143. https://doi.org/10.11896/jsjkx.190700188
[6] 岳晓威, 彭莎, 秦克云.
基于面向对象(属性)概念格的形式背景属性约简方法
Attribute Reduction Methods of Formal Context Based on ObJect (Attribute) Oriented Concept Lattice
计算机科学, 2020, 47(6A): 436-439. https://doi.org/10.11896/JsJkx.191100011
[7] 陈毅宁,陈红梅.
基于距离比值尺度的模糊粗糙集属性约简
Attribute Reduction of Fuzzy Rough Set Based on Distance Ratio Scale
计算机科学, 2020, 47(3): 67-72. https://doi.org/10.11896/jsjkx.190100196
[8] 徐怡,唐静昕.
基于优化可辨识矩阵和改进差别信息树的属性约简算法
Attribute Reduction Algorithm Based on Optimized Discernibility Matrix and Improving Discernibility Information Tree
计算机科学, 2020, 47(3): 73-78. https://doi.org/10.11896/jsjkx.190500125
[9] 侯成军,米据生,梁美社.
基于局部可调节多粒度粗糙集的属性约简
Attribute Reduction Based on Local Adjustable Multi-granulation Rough Set
计算机科学, 2020, 47(3): 87-91. https://doi.org/10.11896/jsjkx.190500162
[10] 郭庆春,马建敏.
对偶区间集概念格上区间集协调集的判定方法
Judgment Methods of Interval-set Consistent Sets of Dual Interval-set Concept Lattices
计算机科学, 2020, 47(3): 98-102. https://doi.org/10.11896/jsjkx.190500098
[11] 韩成成, 林强, 满正行, 曹永春, 王海军, 王维兰.
面向病灶与其表征关联提取的核医学诊断文本挖掘
Mining Nuclear Medicine Diagnosis Text for Correlation Extraction Between Lesions and Their Representations
计算机科学, 2020, 47(11A): 524-530. https://doi.org/10.11896/jsjkx.200400062
[12] 龙柄翰, 徐伟华, 张晓燕.
不协调目标信息系统中基于改进差别信息树的分布属性约简
Distribution Attribute Reduction Based on Improved Discernibility Information Tree in Inconsistent System
计算机科学, 2019, 46(6A): 115-119.
[13] 李艳, 张丽, 陈俊芬.
动态信息系统中基于序贯三支决策的属性约简方法
Attribute Reduction Method Based on Sequential Three-way Decisions in Dynamic Information Systems
计算机科学, 2019, 46(6A): 120-123.
[14] 李艳, 张丽, 王雪静, 陈俊芬.
优势-等价关系下序贯三支决策的属性约简
Attribute Reduction for Sequential Three-way Decisions Under Dominance-Equivalence Relations
计算机科学, 2019, 46(2): 242-148. https://doi.org/10.11896/j.issn.1002-137X.2019.02.037
[15] 姜泽华, 王怡博, 徐刚, 杨习贝, 王平心.
面向多尺度的属性约简加速器
Multi-scale Based Accelerator for Attribute Reduction
计算机科学, 2019, 46(12): 250-256. https://doi.org/10.11896/jsjkx.181102031
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!