[1]王雯,康向平,武燕.概念格在不完备形式背景中的知识获取模型[J].智能系统学报,2019,14(05):1048-1055.[doi:10.11992/tis.201809021]
 WANG Wen,KANG Xiangping,WU Yan.Knowledge acquisition model of concept lattice in an incomplete formal context[J].CAAI Transactions on Intelligent Systems,2019,14(05):1048-1055.[doi:10.11992/tis.201809021]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年05期
页码:
1048-1055
栏目:
出版日期:
2019-09-05

文章信息/Info

Title:
Knowledge acquisition model of concept lattice in an incomplete formal context
作者:
王雯12 康向平34 武燕2
1. 太原工业学院 自动化系, 山西 太原 030008;
2. 太原理工大学 信息工程学院, 山西 太原 030024;
3. 同济大学 嵌入式系统与服务计算教育部重点实验室, 上海 201804;
4. 同济大学 计算机科学与技术系, 上海 201804
Author(s):
WANG Wen12 KANG Xiangping34 WU Yan2
1. Department of Automation, Taiyuan Industrial College, Taiyuan 030008, China;
2. College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
3. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China;
4. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
关键词:
概念格粗糙集不完备形式背景等价类极大相容类信息粒化数据处理
Keywords:
concept latticerough setincomplete formal contextequivalence classmaximal tolerance classinformation granulationdata processing
分类号:
TP18
DOI:
10.11992/tis.201809021
摘要:
为了使概念格模型具有更强的数据处理能力,消除不完备信息带来的影响,针对经典概念格的局限性,本文将粗糙集中的粒化思维融入到概念格中。首先探讨了概念格视角下的信息粒化方法,然后提出了基于等价类和基于极大相容类的知识获取方法,最后给出了实例分析。这些方法一方面有助于概念格与粗糙集的融合,另一方面也为探索不完备形式背景的分析处理机制提供了有益思路。
Abstract:
To improve the data processing ability of the concept lattice model and eliminate the impact of incomplete information, in this paper, considering the limitations of classical concept lattice in actual applications, the granulation idea in rough sets is integrated into concept lattice. First, we explore information granulation methods from the perspective of concept lattice and then propose a knowledge acquisition method based on the equivalence class and maximal tolerance class, and then, carry out the case analysis. These methods are helpful for the integration of concept lattice and rough sets. Moreover, they also provide some useful ideas for exploring the analysis and processing mechanism of incomplete formal contexts.

参考文献/References:

[1] WILLE R. Restructuring lattice theory:an approach based on hierarchies of concepts[M].RIVAL I. Ordered Sets. Dordrecht:Reidel, 1982:445-470.
[2] 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.
[3] 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
[4] 王国胤. Rough集理论在不完备信息系统中的扩充[J]. 计算机研究与发展, 2002, 39(10):1238-1243 WANG Guoyin. Extension of rough set under incomplete information systems[J]. Journal of computer research and development, 2002, 39(10):1238-1243
[5] KRYSZKIEWICZ M. Rough set approach to incomplete information systems[J]. Information sciences, 1998, 112(1/2/3/4):39-49.
[6] KRYSZKIEWICZ M. Rules in incomplete information systems[J]. Information sciences, 1999, 113(3/4):271-292.
[7] STEFANOWSKI J, TSOUKIÀS A. Incomplete information tables and rough classification[J]. Computational intelligence, 2001, 17(3):546-566.
[8] LEUNG Y, LI Deyu. Maximal consistent block technique for rule acquisition in incomplete information systems[J]. Information sciences, 2003, 153:85-106.
[9] ZHANG Wenxiu, MI Jusheng. Incomplete information system and its optimal selections[J]. Computers & mathematics with applications, 2004, 48(5/6):691-698.
[10] 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.
[11] LI Jinhai, MEI Changlin, LV Yuejin. Incomplete decision contexts:approximate concept construction, rule acquisition and knowledge reduction[J]. International journal of approximate reasoning, 2013, 54(1):149-165.
[12] LI Meizheng, WANG Guoyi. Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts[J]. Knowledge-based systems, 2016, 91:165-178.
[13] 张慧雯, 刘文奇, 李金海. 完备形式背景下近似概念格的公理化方法[J]. 计算机科学, 2015, 42(6):67-70, 92 ZHANG Huiwen, LIU Wenqi, LI Jinhai. Axiomatic characterizations of approximate concept lattices in incomplete contexts[J]. Computer science, 2015, 42(6):67-70, 92
[14] 智慧来. 不完备形式背景上的知识表示[J]. 计算机科学, 2015, 42(1):276-278 ZHI Huilai. Knowledge representation on incomplete formal context[J]. Computer science, 2015, 42(1):276-278
[15] 李磊军, 李美争, 解滨, 等. 三支决策视角下概念格的分析和比较[J]. 模式识别与人工智能, 2016, 29(10):951-960 LI Leijun, LI Meizheng, XIE Bin, et al. Analysis and comparison of concept lattices from the perspective of three-way decisions[J]. Pattern recognition and artificial intelligence, 2016, 29(10):951-960
[16] 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
[17] GANTER B, WILLE R. Formal concept analysis:mathematical foundations[M]. Berlin:Springer-Verlag, 1999.

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备注/Memo

备注/Memo:
收稿日期:2018-09-13。
基金项目:国家自然科学基金项目(61603278).
作者简介:王雯,女,1984年生,硕士研究生,主要研究方向为智能控制理论及应用、粒计算;康向平,男,1982年生,博士研究生,主要研究方向为粗糙集、概念格、粒计算;武燕,女,1982年生,硕士研究生,主要研究方向为智能信息处理。
通讯作者:王雯.E-mail:wangwen80971@163.com
更新日期/Last Update: 1900-01-01