Computer Science ›› 2017, Vol. 44 ›› Issue (9): 62-66.doi: 10.11896/j.issn.1002-137X.2017.09.012

Previous Articles     Next Articles

Knowledge Discovery Method for Heterogeneous Data Based on Concept Lattice

NIU Jiao-jiao, FAN Min, LI Jin-hai and YIN Yun-qiang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Recently,much attention has been paid to concept-lattice-based knowledge discovery methods.In the meanwhile,this topic has attracted many research interests from the communities of formal concept analysis and rough set theory.Especially,in recent years,some substantial progresses have been made on studying formal decision contexts.However,the existing knowledge discovery methods are lack of feasibility and effectiveness when they are applied to big data.Considering that heterogeneity is one of the main characteristics of big data,this paper investigated concept-lattice-based knowledge discovery methods for heterogeneous data.Specifically,the notion of a heterogeneous formal context was proposed as well as its corresponding concept lattice,heterogeneous formal contexts were further employed to define heterogeneous formal decision contexts,and rule acquisition was discussed.Moreover,an algorithm of mining non-redundant decision rules from a heterogeneous formal decision context was explored.

Key words: Concept lattice,Heterogeneous formal context,Heterogeneous formal decision context,Knowledge discovery

[1] WILLE R.Restructuring lattice theory:an approach based on hierarchies of concepts[M]∥Rival I,ed.Ordered Sets.Dordrecht-Boston:Reidel,1982:445-470.
[2] HU K Y,LU Y C,SHI C Y.Advances in concept lattice and its application[J].Journal of Tsinghua University (Science and Technology),2000,40(9):77-81.(in Chinese) 胡可云,陆玉昌,石纯一.概念格及其应用进展[J].清华大学学报(自然科学版),2000,40(9):77-81.
[3] ZHANG W X,WEI L,QI J J.Attribute reduction theory and approach to concept lattice[J].Science China Series F-Information Sciences,2005,35(6):628-639.(in Chinese) 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(F辑):信息科学,2005,35(6):628-639.
[4] LI J H,MEI C L,XU W H,et al.Concept learning via granular computing:A cognitive viewpoint[J].Information Sciences,2015,298:447-467.
[5] XU W H,LI W T.Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets [J].IEEE Transactions on Cybernetics,2016,46(2):366-379.
[6] ZHANG T,REN H L,HONG W X,et al.The visualizing calculation of formal concept that based on the attribute topologies [J].Acta Electronica Sinica,2014,42(5):925-932.(in Chinese) 张涛,任宏雷,洪文学,等.基于属性拓扑的可视化形式概念计算 [J].电子学报,2014,42(5):925-932.
[7] QI J J,WEI L,YAO Y Y.Three-way formal concept analysis [M]∥ Rough Sets and Knowledge Technology.2014:732-741.
[8] WEI L,QI J J,ZHANG W X.Attribute reduction theory of concept lattice based on decision formal contexts[J].Science China Series F-Information Sciences,2008,38(2):195-208.(in Chinese) 魏玲,祁建军,张文修.决策形式背景的概念格属性约简[J].中国科学(F辑):信息科学,2008,38(2):195-208.
[9] SHAO M W,LEUNG Y,WU W Z.Rule acquisition and complexity reduction in formal decision contexts[J].International Journal of Approximate Reasoning,2014,55(1):259-274.
[10] LI J H,MEI C L,LV Y J.A heuristic knowledge-reductionmethod for decision formal contexts[J].Computers and Mathematics with Applications,2011,61(4):1096-1106.
[11] LI J H,MEI C L,CHERUKURI A K,et al.On rule acquisition in decision formal contexts [J].International Journal of Machine Learning and Cybernetics,2013,4(6):721-731.
[12] WU W Z,LEUNG Y,MI J S.Granular computing and know-ledge reduction in formal contexts[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(10):1461-1474.
[13] 张文修,仇国芳.基于粗糙集的不确定性决策[M].北京:清华大学出版社,2005.
[14] QU K S,ZHAI Y H,LIANG J Y,et al.Study of decision implications based on formal concept analysis[J].International Journal of General Systems,2007,36(2):147-156.
[15] ZHI H L.Extended model of formal concept analysis oriented for heterogeneous data analysis[J].Acta Electronica Sinica,2013,41(12):2451-2455.(in Chinese) 智慧来.面向异构数据分析的形式概念分析的扩展模型[J].电子学报,2013,41(12):2451-2455.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!