[1]胡小康,王俊红.基于相容模糊概念的规则提取方法[J].智能系统学报编辑部,2016,11(3):352-358.[doi:10.11992/tis.201603043]
 HU Xiaokang,WANG Junhong.Research on rule extraction method based on compatibility fuzzy concept[J].CAAI Transactions on Intelligent Systems,2016,11(3):352-358.[doi:10.11992/tis.201603043]
点击复制

基于相容模糊概念的规则提取方法(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年3期
页码:
352-358
栏目:
出版日期:
2016-06-25

文章信息/Info

Title:
Research on rule extraction method based on compatibility fuzzy concept
作者:
胡小康 王俊红
山西大学 计算机与信息技术学院, 山西 太原 030006
Author(s):
HU Xiaokang WANG Junhong
School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
关键词:
形式背景概念格相似模糊概念相容模糊概念知识获取关联规则偏序关系相容关系
Keywords:
formal contextconcept latticeapproximate fuzzy conceptcompatible fuzzy conceptknowledge representationassociation rulespartial ordering relationcompatible relation
分类号:
TP18
DOI:
10.11992/tis.201603043
摘要:
概念格是具有严格数学模型的数据分析与规则提取的一种有效工具,大部分情况下是在完备的精确形式背景即二值背景下进行研究,然而在现实生活中遇到的大多数情况是不完备的模糊形式背景,不完备模糊形式背景中包含许多不确定的信息,其上的知识表示与完备形式背景下的知识表示既有区别又有联系。为了研究两者的内在联系,本文定义了相似模糊概念和相容模糊概念,构建了相似模糊概念格和建立了在不完备模糊形式背景下相容模糊概念之间的偏序关系,进而设计出面向不完备模糊形式背景下的关联规则挖掘算法.最后通过实验验证了该方法的有效性和可行性。
Abstract:
The concept lattice is an effective data analysis and rule extraction tool with a strict mathematical model. In most instances, studies are carried out in a complete formal context, i.e., a two-value context. However, in real life, an incomplete fuzzy formal context is frequently experienced. Incomplete fuzzy contexts contain a lot of uncertain information. There are both distinctions and relationships that can be identified between the forms of knowledge representation in the incomplete fuzzy formal and complete formal contexts. To study their internal relationship, in this paper, we define approximate fuzzy and compatible fuzzy concepts, establish an approximate fuzzy concept lattice, and identify a partial ordering relationship between compatible fuzzy concepts in an incomplete fuzzy formal context. We extend the design of an association rules mining algorithm to address the background of the incomplete fuzzy formal context, and conduct an experiment to demonstrate the feasibility and effectiveness of the proposed method.

参考文献/References:

[1] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[M]//RIVAL I. Ordered sets. Netherlands: Springer, 1982: 445-470.
[2] POELMANS J, IGNATOV D I, KUZNETSOV S O, et al. Formal concept analysis in knowledge processing: a survey on applications[J]. Expert systems with applications, 2013, 40(16): 6538-6560.
[3] MINEAU G W, GODIN R. Automatic structuring of knowledge bases by conceptual clustering[J]. IEEE transactions on knowledge and data engineering, 1995, 7(5): 824-829.
[4] COLE R, EKLUND P W. Scalability in formal concept analysis[J]. Computational intelligence, 1999, 15(1): 11-27.
[5] CARPINETO C, ROMANO G. A lattice conceptual clustering system and its application to browsing retrieval[J]. Machine learning, 1996, 24(2): 95-122.
[6] MA Jianmin, ZHANG Wenxiu. Axiomatic characterizations of dual concept lattices[J]. International journal of approximate reasoning, 2013, 54(5): 690-697.
[7] 胡明涵, 张莉, 任飞亮. 模糊形式概念分析与模糊概念格[J]. 东北大学学报:自然科学版, 2007, 28(9): 1274-1277. HU Minghan, ZHANG Li, REN Feiliang. Fuzzy formal concept analysis and fuzzy concept lattices[J]. Journal of northeastern university : natural science, 2007, 28(9): 1274-1277.
[8] GRZYMALA-BUSSE J W. Rough set approach to incomplete data[C]//Proceedings of the 7th international conference on artificial intelligence and soft computing-ICAISC 2004. Berlin Heidelberg, Germany, 2004: 50-55.
[9] LIU Jun, YAO Xiaoqiu. Formal concept analysis of incomplete information system[C]//Proceedings of the 7th international conference on fuzzy systems and knowledge discovery. Yantai, China, 2010, 5: 2016-2020.
[10] DJOUADI Y, DUBOIS D, PRADE H. Possibility theory and formal concept analysis: Context decomposition and uncertainty handling[C]//Proceedings of the 13th international conference on information processing and management of uncertainty. Berlin Heidelberg, Germany, 2010: 260-269.
[11] KRUPKA M. Fuzzy concept lattices with incomplete knowledge[C]//Proceedings of the 14th international conference on information processing and management of uncertainty in knowledge-based systems. Berlin Heidelberg, Germany, 2012: 171-180.
[12] DJOUADI Y, PRADE H. Interval-valued fuzzy formal concept analysis[C]//Proceedings of the 18th international symposium. Berlin Heidelberg, Germany, 2009: 592-601.
[13] 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.
[14] LAI Hongliang, ZHANG Dexue. Concept lattices of fuzzy contexts: formal concept analysis vs. rough set theory[J]. International journal of approximate reasoning, 2009, 50(5): 695-707.
[15] 何淑贤, 王育红, 翟岩慧, 等. 不完备形式背景及其完备化方法[J]. 山西大学学报:自然科学版, 2006, 29(4): 364-367. HE Shuxian, WANG Yuhong, ZHAI Yanhui, et al. Incomplete formal context and the completion approach[J]. Journal of Shanxi university : natural science edition, 2006, 29(4): 364-367.
[16] 谢志鹏, 刘宗田. 概念格的快速渐进式构造算法[J]. 计算机学报, 2002, 25(5): 490-496. XIE Zhipeng, LIU Zongtian. A fast incremental algorithm for building concept lattice[J]. Chinese journal of computers, 2002, 25(5): 490-496.
[17] 梁吉业, 王俊红. 基于概念格的规则产生集挖掘算法[J]. 计算机研究与发展, 2004, 41(8): 1339-1344. LIANG Jiye, WANG Junhong. An algorithm for extracting rule-generating sets based on concept lattice[J]. Journal of computer research and development, 2004, 41(8): 1339-1344.
[18] LEKHA A, SRIKRISHNA C V, VINOD V. Fuzzy association rule mining[J]. Journal of computer science, 2015, 11(1): 71-74.
[19] LAKHAL L, STUMME G. Efficient mining of association rules based on formal concept analysis[M]//GANTER B, STUMME G, WILLE R. Formal concept analysis. Berlin Heidelberg: Springer-Verlag, 2005: 180-195.
[20] KUMAR CH A, DIAS S M, VIEIRA N J. Knowledge reduction in formal contexts using non-negative matrix factorization[J]. Mathematics and computers in simulation, 2015, 109: 46-63.
[21] 王志海, 胡可云, 胡学纲, 等. 概念格上规则提取的一般算法与渐进式算法[J]. 计算机学报, 1991, 22(1): 66-70. WANG Zhihai, HU Keyun, HU Xuegang, et al. General and incremental algorithms of rule extraction based on concept lattice[J]. Chinese journal of computers, 1991, 22(1): 66-70.

相似文献/References:

[1]杜秋香,张继福,张素兰.概念特化的概念格更新构造算法[J].智能系统学报编辑部,2008,3(05):443.
 DU Qiu-xiang,ZHANG J i-fu,ZHANG Su-lan.An improved algor ithm based on concept spec ialization for constructing concept lattices[J].CAAI Transactions on Intelligent Systems,2008,3(3):443.
[2]马丽,米据生.决策形势背景的命题推演[J].智能系统学报编辑部,2015,10(6):934.[doi:10.11992/tis.201507055]
 MA Li,MI Jusheng.Propositions reasoning of decision formal contexts[J].CAAI Transactions on Intelligent Systems,2015,10(3):934.[doi:10.11992/tis.201507055]
[3]康向平,苗夺谦.一种基于概念格的集值信息系统中的知识获取方法[J].智能系统学报编辑部,2016,11(3):287.[doi:10.11992/tis.201603055]
 KANG Xiangping,MIAO Duoqian.A knowledge acquisition method based on concept latticein set-valued information systems[J].CAAI Transactions on Intelligent Systems,2016,11(3):287.[doi:10.11992/tis.201603055]
[4]刘保相,孟肖丽.基于关联分析的气象云图识别问题研究[J].智能系统学报编辑部,2014,9(05):595.[doi:10.3969/j.issn.1673-4785.201306049]
 LIU Baoxiang,MENG Xiaoli.The study on nephogram recognition based on relational analysis[J].CAAI Transactions on Intelligent Systems,2014,9(3):595.[doi:10.3969/j.issn.1673-4785.201306049]
[5]石慧,何苗,魏玲.基于不可约元下集格的概念获取[J].智能系统学报编辑部,2014,9(02):244.[doi:10.3969/j.issn.1673-4785.201307019]
 SHI Hui,HE Miao,WEI Ling.Concept acquisition based on the down-set lattice of irreducible elements[J].CAAI Transactions on Intelligent Systems,2014,9(3):244.[doi:10.3969/j.issn.1673-4785.201307019]
[6]毛华,刘祎超.基于权值最大圈的概念格构造算法[J].智能系统学报编辑部,2016,11(4):519.[doi:10.11992/tis.201606006]
 MAO Hua,LIU Yichao.An algorithm for concept lattice construction based on maximum cycles of weight values[J].CAAI Transactions on Intelligent Systems,2016,11(3):519.[doi:10.11992/tis.201606006]
[7]温云霞,王俊红.横向拆分形势背景下的快速规则提取方法[J].智能系统学报编辑部,2016,11(4):526.[doi:10.11992/tis.201606008]
 WEN Yunxia,WANG Junhong.Research on a fast method for extracting rules based on horizontal splitting[J].CAAI Transactions on Intelligent Systems,2016,11(3):526.[doi:10.11992/tis.201606008]
[8]毛华,史明.利用二元拟阵Kn图的一种建格方法[J].智能系统学报编辑部,2017,12(03):333.[doi:10.11992/tis.201704022]
 MAO Hua,SHI Ming.A constructive method of lattice using the Kn diagram of binary matroid[J].CAAI Transactions on Intelligent Systems,2017,12(3):333.[doi:10.11992/tis.201704022]
[9]窦林立,展正然.利用二部图生成概念格[J].智能系统学报编辑部,2018,13(05):687.[doi:10.11992/tis.201703026]
 DOU Linli,ZHAN Zhengran.Constructing concept lattice using bipartite graph[J].CAAI Transactions on Intelligent Systems,2018,13(3):687.[doi:10.11992/tis.201703026]

备注/Memo

备注/Memo:
收稿日期:2016-3-19;改回日期:。
基金项目:国家自然科学基金项目(61202018,61303008,61305057).
作者简介:胡小康,男,1991年生,硕士研究生,主要研究方向为形式概念分析、数据挖掘。王俊红,女,1979年生,副教授,主要研究方向形式概念分析、粗糙集和数据挖掘。主持或参与多项国家863计划、国家自然科学基金和省部级等科研项目。发表学术论文10余篇。
通讯作者:王俊红.E-mail:wjhwjh@sxu.edu.cn.
更新日期/Last Update: 1900-01-01