Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 436-439.doi: 10.11896/JsJkx.191100011

• Database & Big Data & Data Science • Previous Articles     Next Articles

Attribute Reduction Methods of Formal Context Based on ObJect (Attribute) Oriented Concept Lattice

YUE Xiao-wei1, PENG Sha2 and QIN Ke-yun1   

  1. 1 College of Mathematic,Southwest Jiaotong University,Chengdu 611756,China
    2 Chengdu Foreign Languages School,Chengdu 610097,China
  • Published:2020-07-07
  • About author:QIN Ke-yun, born in 1962, Ph.D, professor, Ph.D supervisor.His research interests include rough set theory and so on.
  • Supported by:
    This paper was supported by the National Natural Science Foundation of China (61976130,61473239).

PDF (PC)

647

Abstract: Attribute reduction of formal context is one of the important research topics of formal concept analysis.This paper is devoted to the discussion of attribute reduction methods preserving the structures of obJect-oriented concept lattice and property-oriented concept lattice.By the analysis of the related granular concepts,this paper proposes a new Judgement theorem for consistent set based on obJect-oriented concept lattice and attribute-oriented concept lattice.Then,new discernible attribute sets and discernible attribute matrices are established.The attribute reductions preserving the structures of obJect-oriented concept lattice and property-oriented concept lattice are calculated by using the conversion of Boolean logic formula.The proposed method can avoid computing all obJect-oriented and attribute-oriented concept lattices.In addition,the characteristics of attributes with respect to obJect-oriented concept lattice and property-oriented concept lattice are proposed.Some equivalent descriptions of absolutely necessary attributes,relatively necessary attributes,and absolutely unnecessary attributes are provided.

Key words: Attribute reduction, Attribute-oriented concept lattice, Concept lattice, ObJect-oriented concept lattice

CLC Number: 

  • TP182
[1] WILLE R.Restructuring lattice theory:An app- roach based on hierarchies of concepts //Ordered Sets Dordrecht-Boston:Reidel.1982:445-470.
[2] HU K Y,LU Y C,SHI C Y.Progress of concept lattice and its application .Journal of Tsinghua University (Science Edition),2000,40(9):77-81.
[3] CARPINETO C,ROMANO G.A lattice conceptual clustering system and its application to browsing retrieval .Machine learning,1996,10:95-122.
[4] CHEN Y,YAO Y.A multi-view approach for intelligent dataanalysis Based on data operators .Information Sciences,2008,178(1):1-20.
[5] GANTER B,WILLE R.Formal concept analysis.New York:Springer-Verlag,1999.
[6] ZHANG W X,WEI L,QI J J.Attribute reduction theory and approach to concept lattices .Science in China-Information Sciences,2005,35(6):628-639.
[7] QI J J.Attribute reduction in formal contexts based on a new discernibility matrix .Journal of Applied Mathematics and Computing,2009,30(1/2):305-314.
[8] LI L J,LI M Z,JU S M,et al.A simple dicernibility matrix for attribute reduction in formal concept analysis based on granular concepts .Journal of intelligent and Fuzzy Systems,2019:4325-4337.
[9] DUNTSCH I,GEDIGA G.Modal-style operators in qualitative data analysis//Proceedings of the 2002 IEEE International Conference on Data Mining.IEEE,2002:155-162.
[10] YAO Y Y.A comparative study of formal concpt analysis and rough set theory in data analysis//Proceedings of 3rd International Conference on RSCTC’04.Springer Berlin Heidelberg,2004:59-68.
[11] LIU M X.The theory of reduction for obJect oriented concept lattice and attribute oriented concept lattice .Xi’an:Northwest University,2010.
[12] LIU M,SHAO M W,ZHANG W X.Reduction method for concept lattices based on rough set theory and its application .Computers and Mathematics with Applications,2007,53(9):1390-1410.
[13] MEDINA J.Relating attribute reduction in formal,obJect-oriented and property-oriented concept lattice .Computers and Mathematics with Applications,2012,64(6):1992-2002.
[14] WANG J L.A new method of obJect oriented concept lattice attribute reduction .Journal of Basic Science of Textile University,2013(3):355-358.
[15] WANG X.Theory and method of conceptual lattice reduction .Xi’an :Xi’an Jiaotong University,2008.
[16] WANG Q,WEI L,LI T.A new method of lattice construction based on non-reducible elements .Journal of Northwestern University (Science Edition),2013,43(1):10-14.
[17] LIANG X Y,WANG Q,WEI L.The union reduction of obJect-oriented (attribute) concept lattice based of direct view.Journal of North western University (Science Edition),2015,45(3):357-364.
[1] WANG Zi-yin, LI Lei-jun, MI Ju-sheng, LI Mei-zheng, XIE Bin. Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost [J]. Computer Science, 2022, 49(4): 161-167.
[2] LI Yan, FAN Bin, GUO Jie, LIN Zi-yuan, ZHAO Zhao. Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets [J]. Computer Science, 2021, 48(6A): 342-348.
[3] SHEN Xia-jiong, YANG Ji-yong, ZHANG Lei. Attribute Exploration Algorithm Based on Unrelated Attribute Set [J]. Computer Science, 2021, 48(4): 54-62.
[4] WEN Xin, YAN Xin-yi, CHEN Ze-hua. Minimal Optimistic Concept Generation Algorithm Based on Equivalent Relations [J]. Computer Science, 2021, 48(3): 163-167.
[5] ZENG Hui-kun, MI Ju-sheng, LI Zhong-ling. Dynamic Updating Method of Concepts and Reduction in Formal Context [J]. Computer Science, 2021, 48(1): 131-135.
[6] SANG Bin-bin, YANG Liu-zhong, CHEN Hong-mei, WANG Sheng-wu. Incremental Attribute Reduction Algorithm in Dominance-based Rough Set [J]. Computer Science, 2020, 47(8): 137-143.
[7] CHEN Yi-ning,CHEN Hong-mei. Attribute Reduction of Fuzzy Rough Set Based on Distance Ratio Scale [J]. Computer Science, 2020, 47(3): 67-72.
[8] XU Yi,TANG Jing-xin. Attribute Reduction Algorithm Based on Optimized Discernibility Matrix and Improving Discernibility Information Tree [J]. Computer Science, 2020, 47(3): 73-78.
[9] HOU Cheng-jun,MI Ju-sheng,LIANG Mei-she. Attribute Reduction Based on Local Adjustable Multi-granulation Rough Set [J]. Computer Science, 2020, 47(3): 87-91.
[10] YANG Wen-jing,ZHANG Nan,TONG Xiang-rong,DU Zhen-bin. Class-specific Distribution Preservation Reduction in Interval-valued Decision Systems [J]. Computer Science, 2020, 47(3): 92-97.
[11] GUO Qing-chun,MA Jian-min. Judgment Methods of Interval-set Consistent Sets of Dual Interval-set Concept Lattices [J]. Computer Science, 2020, 47(3): 98-102.
[12] LONG Bing-han, XU Wei-hua, ZHANG Xiao-yan. Distribution Attribute Reduction Based on Improved Discernibility Information Tree in Inconsistent System [J]. Computer Science, 2019, 46(6A): 115-119.
[13] LI Yan, ZHANG Li, CHEN Jun-fen. Attribute Reduction Method Based on Sequential Three-way Decisions in Dynamic Information Systems [J]. Computer Science, 2019, 46(6A): 120-123.
[14] LIN Hong, QIN Ke-yun. Attribute Reduction for Decision Formal Contexts Based on Threek-way Decision Rules [J]. Computer Science, 2019, 46(3): 248-252.
[15] LI Yan, ZHANG Li, WANG Xue-jing, CHEN Jun-fen. Attribute Reduction for Sequential Three-way Decisions Under Dominance-Equivalence Relations [J]. Computer Science, 2019, 46(2): 242-148.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.