Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 117-119.

• Intelligent Computing • Previous Articles     Next Articles

Attribute Transfer and Knowledge Discovery Based on Formal Context

ZHENG Shu-fu,YU Gao-feng   

  1. School of Information,Engineering San Ming University,Sanming,Fujian 365004,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: The theory of concept lattice is an effective tool of knowledge representation and knowledge discovery,and is the basis of knowledge representation,knowledge discovery and knowledge acquisition.Based on the formal context information entropy and the importance of attribute theory,this paper discussed the characteristics of knowledge transfer of formal context attributes,obtained the attribute transfer principle based on formal context,and gave the knowledge discovery and application of formal context.

Key words: Attribute transfer, Concept lattice, Formal context, Information entropy, Knowledge discovery

CLC Number: 

  • TP181
[1]TEECE D.Technology transfer by multinational firms:the resource cost of transferring technological know-how[J].The Economic Journal,1997(87):242-261.
[2]RIVAL I.Ordered Sets[M].Berlin Reidel,1982.
[3]GANTER B,WILLE R.Formal Concept Analysis:Mathematical Foundations[M].Berlin:Springer,1999.
[4]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E:信息科学,2005,36(5):628-639.
[5]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),2000,24(6):4-6.
[6]HUANG C C,LI J H,DIAS S M.Attribute significance,consistency measure and attribute reduction in formal concept analysis[J].Neural Network World,2016,26(6):607-623.
[7]LI J H,MEI C L,LV Y J.Knowledge reduction in decision formal contexts[J].Knowledge-Based Systems,2001(24):709-715.
[8]PEI D,MI J S.Attribute reduction in decision formal context based on homomorphism[J].International Journal of Machine Learning & Cybernetics,2011,2(4):289-293.
[9]LI LJ,MI J S,XIE B.Attribute reduction based on maximal rules in decision formal context[J].International Journal of Computational Intelligence Systems,2014,7(6):1044-1053.
[10]汪应洛,李勖.知识的转移特性研究[J].系统工程理论与实践,2002,22(10):8-11.
[11]史开泉,崔玉泉.S-粗集和它的一般结构[J].山东大学学报(理学版),2002,37(6):471-474.
[12]SHI KQ,CHANG T C.One direction S-rough sets[J].International Journal of Fuzzy Mathematics,2003,11(2):525-542.
[13]SHI K Q.Two direction S-rough sets[J].International Journal of Fuzzy Mathematics,2004,12(1):178-181.
[14]史开泉.S-粗集和它的两类基本形式[J].计算机科学,2004,31(10):21-24.
[1] XIA Yuan, ZHAO Yun-long, FAN Qi-lin. Data Stream Ensemble Classification Algorithm Based on Information Entropy Updating Weight [J]. Computer Science, 2022, 49(3): 92-98.
[2] ZHOU Gang, GUO Fu-liang. Research on Ensemble Learning Method Based on Feature Selection for High-dimensional Data [J]. Computer Science, 2021, 48(6A): 250-254.
[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] WANG Xia, PENG Zhi-hua, LI Jun-yu, WU Wei-zhi. Method of Concept Reduction Based on Concept Discernibility Matrix [J]. Computer Science, 2021, 48(1): 125-130.
[6] ZHAO Qin-yan, LI Zong-min, LIU Yu-jie, LI Hua. Cascaded Siamese Network Visual Tracking Based on Information Entropy [J]. Computer Science, 2020, 47(9): 157-162.
[7] LIU Zi-qi, GUO Bing-hui, CHENG Zhen, YANG Xiao-bo and YIN Zi-qiao. Science and Technology Strategy Evaluation Based on Entropy Fuzzy AHP [J]. Computer Science, 2020, 47(6A): 1-5.
[8] YUE Xiao-wei, PENG Sha and QIN Ke-yun. Attribute Reduction Methods of Formal Context Based on ObJect (Attribute) Oriented Concept Lattice [J]. Computer Science, 2020, 47(6A): 436-439.
[9] 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.
[10] WANG Ya-ge, KANG Xiao-dong, GUO Jun, HONG Rui, LI Bo, ZHANG Xiu-fang. Image Compression Method Combining Canny Edge Detection and SPIHT [J]. Computer Science, 2019, 46(6A): 222-225.
[11] ZHANG Fang, ZHAO Shu-liang, WU Yong-liang. Data Scaling Method for Multi-scale Data Mining [J]. Computer Science, 2019, 46(4): 57-65.
[12] 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.
[13] LI Zhong-ling, MI Ju-sheng, XIE Bin. Attribute Reduction in Inconsistent Decision Formal Contexts [J]. Computer Science, 2019, 46(12): 257-260.
[14] ZHU Pei-pei, LONG Min. Recommendation Methods Considering User Indirect Trust and Gaussian Filling [J]. Computer Science, 2019, 46(11A): 178-184.
[15] YAN An, YAN Xin-yi, CHEN Ze-hua. Formal Vector Method of Rule Extraction for Consistent Decision Information System [J]. Computer Science, 2019, 46(10): 236-241.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!