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《山东大学学报(理学版)》 ›› 2020, Vol. 55 ›› Issue (5): 46-54.doi: 10.6040/j.issn.1671-9352.c.2020.003

• • 上一篇    

多源形式背景中的粒结构

李双伶,岳晓威,秦克云*   

  1. 西南交通大学数学学院, 四川 成都 611756
  • 发布日期:2020-05-06
  • 作者简介:李双伶(1996— ),女,硕士研究生,研究方向为概念格与粒计算. E-mail:lishuangling0@163.com*通信作者简介:秦克云(1962— ),男,教授,博士生导师,研究方向为概念格与粗糙集. E-mail:keyunqin@263.net
  • 基金资助:
    国家自然科学基金资助项目(61976130)

Granular structure in multi-source formal contexts

LI Shuang-ling, YUE Xiao-wei, QIN Ke-yun   

  1. School of Mathematics, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
  • Published:2020-05-06

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摘要： 多源数据的知识发现是大数据分析领域的重要研究问题。借助剩余格理论研究多源形式背景的融合形式背景中概念的粒结构。针对多源同域形式背景融合成的L模糊形式背景,刻画了融合形式背景中的可变阈值概念与单源形式背景中的概念之间的关系;给出了融合形式背景的粒约简计算方法并讨论了融合形式背景的粒约简与单源形式背景的粒约简之间的关系。

关键词: 多源形式背景, 模糊形式背景, 可变阈值概念格, 粒约简

Abstract: Knowledge discovery of multi-source data is an important problem that needs to be solved urgently in the field of big data analysis. Based on the theory of residuated lattices, the granular structure of concepts in fusion L fuzzy formal context of multi-source formal contexts is investigated. The relationships between the variable threshold concepts in the fusion L fuzzy formal context and the concepts in the single source formal contexts are surveyed. The granular reduction method for the fusion L fuzzy formal context is presented and the relationship between the granular reduction of this fuzzy formal context and that of single-source formal contexts are investigated.

Key words: multi-source formal context, fuzzy concept context, variable threshold concept lattices, granular reduction

中图分类号: 

  • TP18
[1] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[C] //Ordered Sets. Berlin: Springer, 1982: 445-470.
[2] DUNTSCH I, GEDIGA G. Modal-style operators in qualitative data analysis[C] //KUMAR V, TSUMOTO S, ZHONG Ning, et al. 2002 IEEE International Conference on Data Mining. Maebashi City: IEEE, 2002: 155-162.
[3] YAO Yiyu. A comparative study of formal concept analysis and rough set theory in data analysis[C] //TSUMOTO S, SŁOWINSKI R, KOMOROWSKI J, et al. International Conference on Rough Sets and Current Trends in Computing, RSCTC 2004, Lecture Notes in Computer Science. Berlin: Springer, 2004: 59-68.
[4] YAO Yiyu. Concept lattices in rough set theory[C] //WITOLD P, MUSLIEK P. IEEE Annual Meeting of Fuzzy Information, 2004, Processing NAFIPS '04. Banff, Canada: IEEE, 2004: 796-801.
[5] BURUSCO A, GONZALEZ R F. The study of the L-fuzzy concept lattice[J]. Mathware and Soft Computing, 1994, 1(3):209-218.
[6] BELOHLAVEK R. Fuzzy Galois connections[J]. Math Logic Quart, 1999, 45(4):497-504.
[7] ZHANG Wenxiu, MA Jianmin, FAN Shiqing. Variable threshold concept lattices[J]. Information Sciences, 2007, 177(22):4883-4892.
[8] QI Jianjun, WEI Ling, YAO Yiyu. Three-way formal concept analysis[C] //MIAO D, PEDRYCZ W, SLEZAK D, et al. Rough Sets and Knowledge Technology: 9th International Conference, RSKT 2014. Cham, Switzerland: Springer, 2014: 732-741.
[9] YAO Yiyu, SHE Yanhong. Rough set models in multi-granulation spaces[J]. Information Sciences, 2016, 327(4):40-56.
[10] XU Weihua, YU Jianhang. A novel approach to information fusion in multi-source data sets: a granular computing view point[J]. Information Sciences, 2017, 378(1):410-423.
[11] WU Weizhi, LEUNG Y. Theory and application of granular labelled partitions in multi-scale decision tables[J]. Information Sciences, 2011, 181(18):3878-3897.
[12] WAN Qing, LI Jinhai, WEI Ling et al. Optimal granule level selection: a granule description accuracy viewpoint[J]. International Journal of Approximate Reasoning, 2020, 116(1):85-105.
[13] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS: a multi-granulation rough set[J]. Information Sciences, 2010, 180(6):949-970.
[14] ZHANG Xiaohong, MIAO Duoqian, LIU Caihui, et al. Constructive methods of rough approximation operators and multi-granulation rough sets[J]. Knowledge Based Systems, 2016, 91(1):114-125.
[15] WEI Wei, LIANG Jiye. Information fusion in rough set theory: an overview[J]. Information Fusion, 2019, 48(1):107-118.
[16] HUANG Chenchen, LI Jinhai, MEI Changlin. Three-way concept learning based on cognitive operators: an information fusion view point[J]. International Journal of Approximate Reasoning, 2017, 84(1):1-20.
[17] 李金海,吴伟志,邓硕. 形式概念分析的多粒度标记理论[J]. 山东大学学报(理学版),2019,54(2):30-40. LI Jinhai, WU Weizhi, DENG Shuo. Multi-scale theory in formal concept analysis[J]. Journal of Shandong University(Natural Science), 2019, 54(2):30-40.
[18] 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):58-60. ZENG Wanglin, SHE Yanhong. Object-oriented multi granulation formal concept analysis[J]. Computer Science, 2018, 45(10):58-60.
[19] 杨涵,秦克云. 面向属性(对象)多粒度概念格之间的关系[J]. 计算机科学与探索, 2019[2020-03-19]. http://kns.cnki.net/kcms/detail/11.5602.TP.20190531.1721.004.html. YANG Han, QIN Keyun. The relationship between attribute(object)oriented multi-granularity concept lattices[J]. Journal of Frontiers of Computer Science and Technology, 2019[2020-03-19]. http://kns.cnki.net/kcms/detail/11.5602.TP.20190531.1721.004.html.
[20] 魏玲,王振,钱婷,等. 多源决策形式背景的属性约简[J]. 陕西师范大学学报,2019,47(5):57-63. WEI Ling, WANG Zhen, QIAN Ting, et al. The attribute reduction of multi-source formal decision contexts[J]. Journal of Shaanxi Normal University(Natural Science Edition), 2019, 47(5):57-63.
[21] BELOHLAVEK R. Fuzzy Galois connections[J]. Math Logic Quart, 1999, 45(4):497-504.
[22] WU Weizhi, LEUNG Y, MI Jusheng. Granular computing and knowledge reduction in formal contexts[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(10):1461-1474.
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