JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (4): 105-115.doi: 10.6040/j.issn.1671-9352.0.2018.296

Previous Articles     Next Articles

Dynamic updating of object-oriented granular concepts in formal concept analysis

LI Fen-ning1,2, FAN Min1,2*, LI Jin-hai1,2   

  1. 1. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
  • Published:2019-04-08

PDF (PC)

701

Abstract: Granular computing(GrC)is a new method to simulate humans thinking in the field of computational intelligence research. GrC includes all theories, methods and technologies related to granularity, and it is an effective tool to solve complex problems. Granular concept is an important notion which is defined by combining granular computing and formal concept analysis. In order to update the granular concepts in a dynamic formal context, this paper introduces the notion of an object-oriented granular concept, analyzes how the extent and intent of the object-oriented granular concept are changed, and puts forward approaches to update the object-oriented granular concepts when objects and attributes are gradually removed from a formal context. Finally, some numerical experiments are conducted to show the effectiveness of the proposed algorithms.

Key words: concept lattice, granular computing, granular concept, dynamic updating

CLC Number: 

  • TP18
[1] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[M]. RIVAL I. Ordered Sets. Boston: Reidel, 1982: 445-470.
[2] 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(信息科学),2005, 35(6):628-639. ZHANG Wenxiu, WEI Ling, QI Jianjun. Attribute reduction theory and approach of concept lattices[J]. Scientia Sinica(Informationis), 2005, 35(6):628-639.
[3] GODIN R, MISSAOUI R, ALAOUI H. Incremental concept formation algorithm based on Galois(concept)lattice[J]. Computational Intelligence, 1995, 11:246-267.
[4] KUZNETSOV S O, OBIEDKOV S A. Comparing performance of algorithms for generating concept lattices[J]. Journal of Experimental and Theoretical Artificial Intelligence, 2002, 14(2/3):189-216.
[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, 40(9):77-81. HU Keyun, LU Yuchang, SHI Chunyi. Advances in concept lattice and its application[J]. Journal of Tsinghua University(Science and Technology), 2000, 40(9):77-81.
[6] DUNTSCH I, GEDIGA G. Modal-style operators in qualitative data analysis[C] // Proceedings of the 2002 IEEE International Conference on Data Mining, Washington: IEEE, 2002: 155-162.
[7] LI Jinhai, MEI Changlin, XU Weihua, et al. Concept learning via granular computing: a cognitive viewpoint[J]. Information Sciences, 2015, 298(1):447-467.
[8] XU Weihua, LI Wentao. 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.
[9] 张文修,徐伟华.基于粒计算的认知模型[J].工程数学学报, 2008, 24(6):957-971. ZHANG Wenxiu, XU Weihua. Cognition model based on granular computing[J]. Chinese Journal of Engineering Mathematics, 2008, 24(6):957-971.
[10] 米允龙,李金海,刘文奇,等.MapReduce框架下的粒概念认知学习系统研究[J].电子学报, 2018, 46(2):289-297. MI Yunlong, LI Jinhai, LIU Wenqi, et al. Research on granular concept cognitive learning system under MapReduce framework[J]. Acta Electronica Sinica, 2018, 46(2):289-297.
[11] LI Jinhai, MEI Changlin, 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] QI Jianjun, WEI Ling, YAO Yiyu. Three-way formal concept analysis[J]. Lecture Notes in Computer Science, 2014, 8818:732-741.
[13] HUANG Chenchen, LI Jinhai, MEI Changlin, et al. Three-way concept learning based on cognitive operators: an information fusion viewpoint[J]. International Journal of Approximate Reasoning, 2017, 84(1):1-20.
[14] YAO Yiyu. A comparative study of formal concept analysis and rough set theory in data analysis[C] // Proceedings of 4th International Conference on Rough Sets and Current Trends in Computing, Berlin: Springer, 2004: 59-68.
[15] YAO Yiyu, CHEN Yaohua. Rough set approximations in formal concept analysis[C] // International Conference on Foundations of Intelligent Systems, Berlin: Springer, 2004: 73-78.
[16] LIU Min, SHAO Mingwen, ZHANG Wenxiu, et al. Reduction method for concept lattices based on rough set theory and its application[J]. Computers and Mathematics with Applications, 2007, 53(9):1390-1410.
[17] YAO Yiyu. Concept lattices in rough set theory[C] // Proceedings of the 23rd International Meeting of the North American Fuzzy Information Processing Society, Washington: IEEE, 2004: 796-801.
[18] WANG Xia, ZHANG Wenxiu. Relations of attribute reduction between object and property oriented concept lattices[J]. Knowledge-Based Systems, 2008, 21(5):398-403.
[19] WEI Ling, QIAN Ting. Three-way object oriented concept lattice and the three-way property oriented concept lattice[C] // International Conference on Machine Learning and Cybernetics, Washington: IEEE, 2015: 854-859.
[20] MA Jianmin, LEUNG Yee, ZHANG Wenxiu. Attribute reductions in object-oriented concept lattices[J]. International Journal of Machine Learning & Cybernetics, 2014, 5(5):789-813.
[21] ZADEH L A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets and Systems, 1997, 19:111-127.
[22] YAO Yiyu. Perspectives of granular computing[C] // IEEE International Conference on Granular Computing, Washington: IEEE, 2013: 85-90.
[23] YAO Yiyu. Granular computing: basic issues and possible solutions[C] // Proceedings of the 5th Joint Conference on Information Sciences, Holland: Elsevier, 2000: 186-189.
[24] 梁吉业, 钱宇华, 李玉德,等. 大数据挖掘的粒计算理论与方法[J]. 中国科学(信息科学), 2015, 45(11):1355-1369. LIANG Jiye, QIAN Yuhua, LI Yude, et al. Theory and method of granular computing for big data mining[J]. Scientia Sinica(Informationis), 2015, 45(11):1355-1369.
[25] 徐计, 王国胤, 于洪. 基于粒计算的大数据处理[J]. 计算机学报, 2015, 38(8):1497-1517. XU Ji, WANG Guoyin, YU Hong. Review of big data processing based on granular computing[J]. Chinese Journal of Computers, 2015, 38(8):1497-1517.
[26] 苗夺谦, 徐菲菲, 姚一豫,等. 粒计算的集合论描述[J]. 计算机学报, 2012, 35(2):2351-2363. MIAO Duoqian, XU Feifei, YAO Yiyu, et al. Set-theoretic formulation of granular computing[J]. Chinese Journal of Computers, 2012, 35(2):2351-2363.
[27] 李金海, 吴伟志. 形式概念分析的粒计算方法及其研究展望[J]. 山东大学学报(理学版), 2017, 52(7):1-12. LI Jinhai, WU Weizhi. Granular computing approach for formal concept analysis and its research outlooks[J]. Journal of Shandong University(Natural Science), 2017, 52(7):1-12.
[28] 康向平, 苗夺谦. 基于粒计算的概念格拓展模型[J]. 同济大学学报(自然科学版), 2016,44(7):1113-1120. KANG Xiangping, MIAO Duoqian. Expanded concept lattice based on granular computing[J]. Journal of Tongji University(Natural Science), 2016, 44(7):1113-1120.
[29] WU Weizhi, LEUNG Yee, MI Jusheng. Granular computing and knowledge reduction in formal contexts[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(10):1461-1474.
[30] 徐伟华, 李金海, 魏玲,等. 形式概念分析理论与应用[M]. 北京: 科学出版社, 2016. XU Weihua, LI Jinhai, WEI Ling, et al. Formal concept analysis: theory and application[M]. Beijing: Science Press, 2016.
[1] LI Jin-hai, WU Wei-zhi, DENG Shuo. Multi-scale theory in formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(2): 30-40.
[2] ZHANG En-sheng. Composition and structure on attribute reduction of interval-set concept lattices [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(8): 17-24.
[3] HUANG Tao-lin, NIU Jiao-jiao, LI Jin-hai. Reduct updating method in a dynamic formal context based on granular discernibility attribute matrix [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 13-21.
[4] LI Jin-hai, WU Wei-zhi. Granular computing approach for formal concept analysis and its research outlooks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 1-12.
[5] LI Li, GUAN Tao, LIN He. The hybrid parallel rough set model based on pansystems operators [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 22-29.
[6] LIU Lin, WEI Ling, QIAN Ting. Three-way rules extraction in formal decision contexts with confidence [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(2): 101-110.
[7] CHEN Xue, WEI Ling, QIAN Ting. Attribute reduction in formal decision contexts based on AE-concept lattices [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(12): 95-103.
[8] HUANG Wei-ting, ZHAO Hong, ZHU William. Adaptive divide and conquer algorithm for cost-sensitive attribute reduction [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(8): 98-104.
[9] QIN Li-zhen, LI Jin-hai, WANG Yang-yang. Concept lattice based knowledge discovery and its application to analysis of employment data in universities [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(12): 58-64.
[10] TANG Ya-qiang, FAN Min, LI Jin-hai. Cognitive system model and approach to transformation of information granules under triadic formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 102-106.
[11] ZHANG Chun-ying, WANG Li-ya, LIU Bao-xiang. Dynamic reduction theory for interval concept lattice based on covering and its realization [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 15-21.
[12] MA Yuan-yuan, MENG Hui-li, XU Jiu-cheng, ZHU Ma. Normal distribution of lattice close-degree based on granular computing [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 107-110.
[13] YANG Wei-ping1, LIN Meng-lei2. Information granularity in intuitionistic fuzzy information systems [J]. J4, 2012, 47(1): 87-92.
[14] WANG Bin-di, WEI Ling. The reduction theory of concept lattice based on its associated lattice [J]. J4, 2010, 45(9): 20-26.
[15] ZHANG Qing-hua1,2, XING Yu-ke2, WANG Guo-yin2. Transformation between concept knowledge granule and concept information granule [J]. J4, 2010, 45(9): 1-6.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] LIU Fang-yuan, MENG Xian-jia, TANG Zhan-yong, FANG Ding-yi, GONG Xiao-qing. Android application protection based on smali code obfuscation[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(3): 44 -50 .
[2] LIAO Xiang-wen, ZHANG Ling-ying, WEI Jing-jing, GUI Lin, CHENG Xue-qi, CHEN Guo-long. User influence analysis of social media with temporal characteristics[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(3): 1 -12 .
[3] GU Shen-ming, LU Jin-lu, WU Wei-zhi, ZHUANG Yu-bin. Local optimal granularity selections in generalized multi-scale decision systems[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(8): 1 -8 .
[4] ZHU Lin. Separated monic representations of quivers of type A4and RSS equivalences[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(2): 1 -8 .
[5] . Interval algorithm for mixed integer nonlinear two-level programming problems[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(2): 9 -17 .
[6] YAN Yan, HAO Xiao-hong. Differential privacy partitioning algorithm based on adaptive density grids[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(9): 12 -22 .
[7] YE Xiao-ming, CHEN Xing-shu, YANG Li, WANG Wen-xian, ZHU Yi, SHAO Guo-lin, LIANG Gang. Anomaly detection model of host group based on graph-evolution events[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(9): 1 -11 .
[8] Zhao-xia WU,Jia-qi WANG. Wireless single spectrum secure auction algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(11): 51 -55 .
[9] WAN Peng-fei, GAO Xing-bao. Novel artificial bee colony algorithm based on objective space decomposition for solving multi-objective optimization problems[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(11): 56 -66 .
[10] CAO Wei-dong, DAI Tao, YU Jin-biao, WANG Xiao-hong, SHI An-feng. Improvement on the solution of pressure equation based on alternating direction in chemical flooding model[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(10): 88 -94 .
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd