JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (5): 13-21.doi: 10.6040/j.issn.1671-9352.c.2020.005

Previous Articles    

Approximate concept and rule acquisition based on attribute granularity

HE Xiao-li1,2, SHE Yan-hong1,2   

  1. 1. College of Science, Xian Shiyou University, Xian 710065, Shaanxi, China;
    2. Institute of Concepts, Cognition and Intelligence, Northwest University, Xian 710127, Shaanxi, China
  • Published:2020-05-06

Abstract: This paper introduces the idea of attribute granularity into the study of incomplete context. Firstly, by means of granularity trees and cuts, the relationship between approximate concepts at different levels of granularities is investigated. Next, the relationship between approximate decision rules at different levels of granularities in incomplete context is examined. Lastly, three types of consistences are introduced into incomplete context, and the relationship between different types of consistencs at different levels of granularities is also studied.

Key words: incomplete context, approximate concept, granularity tree, rule acquisiton

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] BURMEISTER P, HOLZER R. On the treatment of incomplete knowledge in formal concept analysis[C] //Conceptual Structures: Logical, Linguist, and Computational Issues. Berlin: Springer, 2000: 385-398.
[3] LI Jinhai, MEI Changlin, LYU Yuejin. Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction[J]. International Journal of Approximate Reasoning, 2013, 54(1):149-165.
[4] LI Meizheng, WANG Guoyin. Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts[J]. Knowledge-Based Systems, 2016, 91:165-178.
[5] YAO Yiyu. Interval sets and three-way concept analysis in incomplete contexts[J]. International Journal of Machine Learning and Cybernetics, 2017, 8(1):3-20.
[6] REN Ruisi, WEI Ling, YAO Yiyu. An analysis of three types of partially-known formal concepts[J]. International Journal of Machine Learning and Cybernetics, 2018, 9(11):1767-1783.
[7] 王振, 魏玲. 基于单边区间集概念格的不完备形式背景的属性约简[J]. 计算机科学, 2018, 45(1):73-78. WANG Zhen, WEI Ling. Attribute reduction of partially-known formal concept lattices for incomplete contexts[J]. Computer Science, 2018, 45(1):73-78.
[8] WU Weizhi, LEUNG Yee, MI Jusheng. Granular computing and knowledge reduction in formal contexts[J]. IEEE Transactions on Knowledge and Date Enginerring, 2009, 21(10):1461-1474.
[9] ZHANG Qinghua, XING Yuke. Formal concept analysis based on granular computing[J]. Journal of Computational Information Systems, 2010, 6(7):2287-2296.
[10] BELOHLAVEK R, BAETS B D, KONECNY J. Granularity of attributes in formal concept analysis[J]. Information Sciences, 2014, 260:149-170.
[11] KANG Xiangping, MIAO Duoqian. A study on information granularity in formal concept analysis based on concept bases[J]. Knowledge-Based Systems, 2016, 105:147-159.
[12] ZOU Ligeng, ZHANG Zuping, LONG Jun. An efficient algorithm for increasing the granularity levels of attributes informal concept analysis[J]. Expert Systems with Applications, 2016, 46:224-235.
[13] LIU Zhicai, LI Bo, PEI Zhen, et al. Formal concept analysis via multi-granulation attributes[C] //2017 12th International Conference on Intelligent Systems and Knowledge Engineering. Nanjing: IEEE, 2017: 1-6.
[14] 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.
[15] 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 multigrnulation formal concept analysis[J]. Computer Science, 2018, 45(10):51-53.
[16] QI Jianjun, WEI Ling, WAN Qing. Multi-level granularity in formal concept analysis[J]. Granular Computing, 2019, 3(4):351-362.
[17] SHAO Mingwen, LYU Mengmeng, LI Kewen, et al. The construction of attribute(object)-oriented multi-granularity concept lattices[J/OL]. International Journal of Machine Learning and Cybernetics, 2019[2019-05-07]. https://doi.org/10.1007/s13042-019-00955-0.
[18] 钱婷, 赵思雨, 贺晓丽. 基于属性粒度研究决策形式背景的规则提取理论[J]. 山东大学学报(理学版), 2019,54(10):113-120. QIAN Ting, ZHAO Siyu, HE Xiaoli. Rules acquisition of decision formal contexts based on attribute granular[J]. Journal of Shandong University(Natural Science), 2019, 54(10):113-120.
[19] 贺晓丽, 刘华丽, 刘瑶瑶. 多粒度数据的区间形式概念分析法[J].计算机工程与应用,2019,55(19):52-57. HE Xiaoli, LIU Huali, LIU Yaoyao. Internal formal concept analysis approach for multigranulation date[J]. Computer Engineering and Applications, 2019, 55(19):52-57.
[20] 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E辑:信息科学,2008,38(2):195-208. WEI Ling, QI Jianjun, ZHANG Wenxiu. Attribute reduction theory of concept lattice based on decision formal contexts[J]. Science in China Series E: Information Science, 2008, 38(2):195-208.
[21] WANG Hong, WU Weizhi. Knowledge reduction in generalized consistent decision formal contexts[C] //International Conference on Rough Sets and Knowledge Technology. Toronto: Springer, 2007: 364-371.
[1] LI Tong-jun, HUANG Jia-wen, WU Wei-zhi. Attribute reduction of incomplete contexts based on similarity relations [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(8): 9-16.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!