JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (2): 30-40.doi: 10.6040/j.issn.1671-9352.9.2018.002

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Multi-scale theory in formal concept analysis

LI Jin-hai1,2, WU Wei-zhi3,4, DENG Shuo1,2   

  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;
    3. School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China;
    4. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China
  • Published:2019-02-25

Abstract: By using forward and backward scaling approaches, the transformation relationship between information systems and formal contexts was clarified, and the notion of a multi-scale formal context was formally defined. It was verified that multi-scale formal contexts and multi-scale information systems could semantically be equivalent to each other. As for a multi-scale formal context, implication rules obtained from different scales were induced by each other. The obtained results could provide a theoretical reference for the further research of multi-scale approaches in formal concept analysis.

Key words: granular computing, rough set, concept lattice, formal context, multi-scale

CLC Number: 

  • TP18
[1] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[C] //Ordered Sets. Berlin: Springer, 1982: 445-470.
[2] PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[3] YAO Yiyu. Concept lattices in rough set theory[C] //Proceedings of 2004 Annual Meeting of the North American Fuzzy Information Processing Society. Washington: IEEE, 2004: 796-801.
[4] 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(RSCTC 2004). Berlin: Springer, 2004: 59-68.
[5] 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.
[6] MEDINA J. Relating attribute reduction in formal, object-oriented and property-oriented concept lattices[J]. Computers and Mathematics with Applications, 2012, 64(6):1992-2002.
[7] YAO Yiyu. Rough-set concept analysis: interpreting RS-definable concepts based on ideas from formal concept analysis [J]. Information Sciences, 2016, 346-347: 442-462.
[8] 智慧来, 李金海. 基于必然属性分析的粒描述[J]. 计算机学报, 2018, 41(12):2702-2719. ZHI Huilai, LI Jinhai. Granule description based on necessary attribute analysis[J]. Chinese Journal of Computers, 2018, 41(12):2702-2719.
[9] CHEN Jinkun, LI Jinjin, LIN Yaojin, et al. Relations of reduction between covering generalized rough sets and concept lattices[J]. Information Sciences, 2015, 304: 16-27.
[10] TAN Anhui, LI Jinjin, LIN Guoping. Connections between covering-based rough sets and concept lattices[J]. International Journal of Approximation Reasoning, 2015, 56: 43-58.
[11] WANG Xia, ZHANG Wenxiu. Relations of attribute reduction between object and property oriented concept lattice [J]. Knowledge-Based Systems, 2008, 21(5): 398-403.
[12] 魏玲, 祁建军, 张文修. 概念格与粗糙集的关系研究[J]. 计算机科学, 2006, 33(3):18-21. WEI Ling, QI Jianjun, ZHANG Wenxiu. Study on relationships between concept lattice and rough set[J]. Computer Science, 2006, 33(3): 18-21.
[13] 王俊红, 梁吉业. 概念格与粗糙集[J]. 山西大学学报(自然科学版), 2003, 26(4): 307-310. WANG Junhong, LIANG Jiye. Concept lattice and rough set [J]. Journal of Shanxi University(Natural Science Edition), 2003, 26(4): 307-310.
[14] 仇国芳, 张志霞, 张炜.基于粗糙集方法的概念格理论研究综述[J]. 模糊系统与数学, 2014, 28(1): 168-177. QIU Guofang, ZHANG Zhixia, ZHANG Wei. A survey for study on concept lattice theory via rough set[J]. Fuzzy Systems and Mathematics, 2014, 28(1): 168-177.
[15] 张文修, 姚一豫, 梁怡. 粗糙集与概念格[M]. 西安: 西安交通大学出版社, 2006. ZHANG Wenxiu, YAO Yiyu, LIANG Yi. Rough set and concept lattice[M]. Xian: Xian Jiaotong University Press, 2006.
[16] 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.
[17] ZADEH L A.Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets and Systems, 1997, 90(2): 111-127.
[18] LIN T Y. Granular computing on binary relations I: data mining and neighborhood systems[C] //Rough Sets in Knowledge Discovery. Berlin: Spring, 1998: 107-121.
[19] YAO Yiyu. Information granulation and rough set approximation[J]. International Journal of Intelligent Systems, 2001, 16: 87-104.
[20] PEDRYCZ W, SKOWRON A, KREINOVICH V. Handbook of granular computing[M]. New York: Wiley, 2008.
[21] 苗夺谦, 王国胤, 刘清, 等. 粒计算: 过去、现在与展望[M]. 北京: 科学出版社, 2007. MIAO Duoqian, WANG Guoyin, LIU Qing, et al. Granular computing: past, present and fature[M]. Beijing: Science Press, 2007.
[22] 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.
[23] 梁吉业, 钱宇华, 李德玉, 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 Deyu, et al. Theory and method of granular computing for big data mining [J]. Science China(Information Sciences), 2015, 45(11): 1355-1369.
[24] 王国胤, 张清华, 马希骜, 等. 知识不确定性问题的粒计算模型[J]. 软件学报, 2011, 22(4): 676-694. WANG Guoyin, ZHANG Qinghua, MA Xi’ao, et al. Granular computing models for knowledge uncertainty [J]. Journal of Software, 2011, 22(4): 676-694.
[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] 张燕平, 张铃, 吴涛.不同粒度世界的描述法——商空间法[J]. 计算机学报, 2004, 27(3): 238-333. ZHANG Yanping, ZHANG Ling, WU Tao. The representation of different granular worlds: a quotient space [J]. Chinese Journal of Computers, 2004, 27(3): 238-333.
[27] MA Jianmin, ZHANG Wenxiu, LEUNG Yee, et al. Granular computing and dual Galois connection[J]. Information Sciences, 2007, 177(23): 5365-5377.
[28] 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.
[29] 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.
[30] GUO Lankun, HUANG Fangping, LI Qingguo, et al. Power contexts and their concept lattices[J]. Discrete Mathematics, 2011, 311: 2049-2063.
[31] WEI Ling, WAN Qing. Granular transformation and irreducible element judgment theory based on pictorial diagrams[J]. IEEE Transactions on Cybernetics, 2017, 46(2): 380-387.
[32] KANG Xiangping, MIAO Duoqian. A variable precision rough set model based on the granularity of tolerance relation [J]. Knowledge-Based Systems, 2016, 102: 103-115.
[33] 马垣, 曾子维, 迟呈英, 等. 形式概念及其新进展[M]. 北京: 科学出版社, 2011. MA Yuan, ZENG Ziwei, CHI Chengying, et al. Formal concept and its new progress[M]. Beijing: Science Press, 2011.
[34] LI Jinhai, REN Yue, MEI Changlin, et al. A comparative study of multigranulation rough sets and concept lattices via rule acquisition[J]. Knowledge-Based Systems, 2016, 91: 152-164.
[35] SHAO Mingwen, LEUNG Yee, WANG Xizhao, et al. Granular reducts of formal fuzzy contexts[J]. Knowledge-Based Systems, 2016, 114: 156-166.
[36] ZHANG Qinghua, XING Yuke. Formal concept analysis based on granular computing[J]. Journal of Computational Information Systems, 2010, 6(7): 2287-2296.
[37] 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. Cognitive model based on granular computing[J]. Chinese Journal of Engineering Mathematics, 2007, 24(6): 957-971.
[38] XU Weihua, PANG Jinzhong, LUO Shuqun. A novel cognitive system model and approach to transformation of information granules[J]. International Journal of Approximate Reasoning, 2014, 55(3): 853-866.
[39] 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.
[40] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS: a multi-granulation rough set[J]. Information Sciences, 2010, 180: 949-970.
[41] QIAN Yuhua, LI Shunyong, LIANG Jiye, et al. Pessimistic rough set based decisions: a multigranulation fusion strategy [J]. Information Sciences, 2014, 264: 196-210.
[42] WU Weizhi, LEUNG YEE. Theory and applications of granular labeled partitions in multi-scale decision tables[J]. Information Sciences, 2011, 181: 3878-3897.
[43] WU Weizhi, QIAN Yuhua, LI Tongjun, et al. On rule acquisition in incomplete multi-scale decision tables[J]. Information Sciences, 2017, 378: 282-302.
[44] SHE Yanhong, LI Jinhai, YANG Hailong. A local approach to rule induction in multi-scale decision tables[J]. Knowledge-Based Systems, 2015, 89: 398-410.
[45] LI Feng, HU Baoqing. A new approach of optimal scale selection to multi-scale decision tables[J]. Information Sciences, 2017, 381: 193-208.
[46] LI Feng, HU Baoqing, WANG Jun. Stepwise optimal scale selection for multi-scale decision tables via attribute significance[J]. Knowledge-Based Systems, 2017, 129: 4-16.
[47] HAO Chen, LI Jinhai, FAN Min, et al. Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions[J]. Information Sciences, 2017, 415/416: 213-232.
[48] LUO Chuan, LI Tianrui, HUANG Yanyong, et al. Updating three-way decisions in incomplete multi-scale information systems[J]. Information Sciences, 2019, 476: 274-289.
[49] 吴伟志, 高仓健, 李同军. 序粒度标记结构及其粗糙近似[J]. 计算机研究与发展, 2014, 51(12): 2623-2632. WU Weizhi, GAO Cangjian, LI Tongjun. Ordered granular labeled structures and rough approximations[J]. Journal of Computer Research and Development, 2014, 51(12): 2623-2632.
[50] 顾沈明, 顾金燕, 吴伟志, 等. 不完备多粒度决策系统的局部最优粒度选择[J]. 计算机研究与发展, 2017, 54(7): 1500-1509. GU Shenming, GU Jinyan, WU Weizhi, et al. Local optimal granularity selections in incomplete multi-granular decision systems[J]. Journal of Computer Research and Development, 2017, 54(7): 1500-1509.
[51] 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.
[52] 郝晨, 范敏, 李金海, 等. 多标记背景下基于粒标记规则的最优标记选择[J]. 模式识别与人工智能, 2016, 29(3): 272-280. HAO Chen, FAN Min, LI Jinhai, et al. Optimal scale selection in multi-scale contexts based on granular scale rules[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(3): 272-280.
[53] XIE Junping, YANG Minhua, LI Jinhai, et al. Rule acquisition and optimal scale selection in multi-scale formal decision contexts and their applications to smart city[J]. Future Generation Computer Systems, 2018, 83: 564-581.
[54] BELOHLAVEK R, DE BAETS B, KONECNY J. Granularity of attributes in formal concept analysis[J]. Information Sciences, 2014, 260: 149-170.
[55] 曾望林, 折延宏. 面向对象的多粒度形式概念分析[J]. 计算机科学, 2018, 45(10): 51-53. ZENG Wanglin, SHE Yanhong. Object-oriented multigranulation formal concept analysis[J]. Computer Science, 2018, 45(10): 51-53.
[56] 张文修, 仇国芳. 基于粗糙集的不确定决策[M]. 北京:清华大学出版社, 2005. ZAHG Wenxiu, QIU Guofang. Uncertain decision making based on rough set[M]. Beijing: Tshinghua University Press, 2005.
[57] QU Kaishe, ZHAI Yanhui, LIANG Jiye, et al. Study of decision implications based on formal concept analysis[J]. International Journal of General Systems, 2007, 36(2): 147-156.
[58] GANTER B, WILLE R. Formal concept analysis: mathematical foundations[M]. New York: Springer, 1999.
[59] QI Jianjun, WEI Ling, YAO Yiyu. Three-way formal concept analysis[C] //Rough Sets and Knowledge Technology. Berlin: Springer, 2014: 732-741.
[60] 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.
[61] LI Jinhai, MEI Changlin, L(¨overU)Yuejin. Knowledge reduction in decision formal contexts [J]. Knowledge-Based Systems, 2011, 24(5): 709-715.
[62] LI Jinhai, HUANG Chenchen, MEI Changlin, et al. An intensive study on rule acquisition in formal decision contexts based on minimal closed label concept lattices[J]. Intelligent Automation & Soft Computing, 2017, 23(3): 519-533.
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