[1]张继福,张素兰,胡立华.约束概念格及其构造方法[J].智能系统学报,2006,1(2):31-38.
ZHANG Ji-fu,ZHANG Su-lan,HU Li-hua.Constrained concept lattice and its construction method[J].CAAI Transactions on Intelligent Systems,2006,1(2):31-38.
点击复制
ZHANG Ji-fu,ZHANG Su-lan,HU Li-hua.Constrained concept lattice and its construction method[J].CAAI Transactions on Intelligent Systems,2006,1(2):31-38.
约束概念格及其构造方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
1
期数:
2006年第2期
页码:
31-38
栏目:
学术论文—人工智能基础
出版日期:
2006-10-25
- Title:
- Constrained concept lattice and its construction method
- 文章编号:
- 1673-4785(2006)02-0031-08
- Keywords:
- data mining; constrained concept lattice; predicate logic; background knowledge; star spectra data
- 分类号:
- TP311
- 文献标志码:
- A
- 摘要:
- 概念格是一种有效的数据分析和知识提取的形式化工具.然而，随着要处理的数据量的剧增，基于原始形式背景构造出的概念格结点数目庞大，占用大的存储空间，同时概念格结点中一些属性集形成的内涵，用户并不都感兴趣，因而从中提取用户需求知识费时.为了降低概念格构造的时空复杂性，增强实用性和针对性，首先采用谓词逻辑描述用户感兴趣的背景知识，并将背景知识引入到概念格结构中,提出了一种新的概念格：约束概念格.在此基础上，提出了基于背景知识的约束概念格构造算法CCLA.理论分析表明，该算法能有效地减少概念格的存储空间和建格时间.最后, 采用恒星天体光谱数据作为形式背景，实验验证了该算法的有效性.
- Abstract:
- Concept lattice is an effective formal tool for data analysis and knowledge min ing. However, with the increase of data volume, the node number of the construct ed concept lattice from the original formal context usually increases enormously , and large storage is required accordingly. Meantime, users are not interested in all intensions of attributes set, and more computational time is unnecessaril y consumed as a result. In order to reduce time and storage complexity and impro ve the utility and pertinence to the concept lattice construction,predicate log ic is used to describe the user interested background knowledge, and a new conce pt lattice structureconstrained concept lattice is presented. Then based on the background knowledge, a construction algorithm (CCLA) is also provided. Thr ough some theoretical analysis, it is shown that the proposed algorithm can redu ce the storage and time complexity of con cept lattice construction process. Finally, the experiments with celestial body spectra as the formal context validate the proposed algorithm.
- 参考文献/References:
-
［1］WILLE R.Restructuring lattice theory: an approaches based on hi erar chies of concepts［A］. In:Rival I ed.Ordered Sets［M］. Dordrecht: Reideal,198 2 .
［2］GODIN R, MISSAOUI R. An Incremental concept formation approach fo r learning from databases ［J］ . Theoretical Computer Science, 1994，133(2)：38 7-419.
［3］BELEN D A，PEDRO A. Formal concept analysis as a support techniq ue for CBR［J］. Knowledgebased Systems,2001,14（3）:163-171.
［4］YOUNG P. Software retrieval by samples using concept analysis ［J］ .The Journal of Systems and Computer，2000，54(3)：179-183.［5］CHU S, CESNIK B. Knowledge representation and retrieval usin g conceptual graphs and free document selforganisation techniques［J］.Interna tional Journal of Medical Informatics，2001，62(2/3)：121-33.
［6］KENT R E. Rough concept analysis［A］.In: Ziarko W P ed. Rough Sets, F uzzy Sets and Knowledge Discovery (RSKD，93)［C］. London : SpringerVerlag , 1 9 94.
［7］CARPINETO C, ROMANO G. A lattice conceptual clustering system and its application to browsing retrieval ［J］ . Maching Learning, 1996,24（2）:95- 122.
［8］王志海,胡可云,胡学钢，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,1999,22(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, 19 99,22(1):66-70.［9］陈世权,程里春.模糊概念格［J］.模糊系统与数学, 2002, 16（4）:12-18 . CHEN Shiquan,CHENG Lichun. Fuzzy concept lattice［J］. Fuzzy Systems and Mat hematics, 2002, 16（4）: 12-18.
［10］NOURINE L,RAYNAUD O. A fast algorithm for building lattices［J］.In formation Processing Letters,1999,71(5-6):199-204.
［11］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）:490-495. XIE Zhipeng,LIU Zongtian. A fast incremental algorithm for building concept la ttice［J］.Chinese Journal of Computers, 2002, 25（5）:490-495.
［12］HAN J, KAMBR M.Data mining concepts and techniques ［M］. San Frans isco:Morgan K aufmann Publishers，2000.
［13］HAN J，LAKS V S，Raymond T. ConstraintBased Multidimensional data Mining ［J］ .Compuer，1999，32(8)：46-50.［14］张 凯,胡运发,王 瑜. 基于互关联后继树的概念格构造算法［J］. 计算机研究与发展, 2004,41(9):1493-1499. ZHANG Kai, HU Yunfa,WANG Yu. An IRSTbased algorithm for construction of c oncept lattices［J］. Journal of Computer Research and Development, 2004, 41(9): 1493-1499
［15］覃冬梅.天体光谱信号的自动识别方法研究［D］.北京：中国科学院自动化研究所，2003. QIN Dongmei. The research on automatic recognition of astronomical spectra data ［D］.Beijing:Institute of Automation Chinese Academy of Sciences，200 3.
- 相似文献/References:
-
[1]王国胤,张清华,胡? 军.粒计算研究综述[J].智能系统学报,2007,2(6):8.
WANG Guo-yin,ZHANG Qing-hua,HU Jun.An overview of granular computing[J].CAAI Transactions on Intelligent Systems,2007,2():8.
[2]何清.物联网与数据挖掘云服务[J].智能系统学报,2012,7(3):189.
HE Qing.The Internet of things and the data mining cloud service[J].CAAI Transactions on Intelligent Systems,2012,7():189.
[3]李海林,郭韧,万校基.基于特征矩阵的多元时间序列最小距离度量方法[J].智能系统学报,2015,10(3):442.[doi:10.3969/j.issn.1673-4785.201405047]
LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10():442.[doi:10.3969/j.issn.1673-4785.201405047]
[4]申彦,朱玉全.CMP上基于数据集划分的K-means多核优化算法[J].智能系统学报,2015,10(4):607.[doi:10.3969/j.issn.1673-4785.201411036]
SHEN Yan,ZHU Yuquan.An optimized algorithm of K-means based on data set partition on CMP systems[J].CAAI Transactions on Intelligent Systems,2015,10():607.[doi:10.3969/j.issn.1673-4785.201411036]
[5]汤建国,汪江桦,韩莉英,等.基于覆盖粗糙集的语言动力系统[J].智能系统学报,2014,9(2):229.[doi:10.3969/j.issn.1673-4785.201307018]
TANG Jianguo,WANG Jianghua,HAN Liying,et al.Linguistic dynamic systems based on covering-based rough sets[J].CAAI Transactions on Intelligent Systems,2014,9():229.[doi:10.3969/j.issn.1673-4785.201307018]
[6]石磊,杜军平,周亦鹏,等.在线社交网络挖掘与搜索技术研究[J].智能系统学报,2016,11(6):777.[doi:10.11992/tis.201612007]
SHI Lei,DU Junping,ZHOU Yipeng,et al.A survey on online social network mining and search[J].CAAI Transactions on Intelligent Systems,2016,11():777.[doi:10.11992/tis.201612007]
[7]淦文燕,刘冲.一种改进的搜索密度峰值的聚类算法[J].智能系统学报,2017,12(2):229.[doi:10.11992/tis.201512036]
GAN Wenyan,LIU Chong.An improved clustering algorithm that searches and finds density peaks[J].CAAI Transactions on Intelligent Systems,2017,12():229.[doi:10.11992/tis.201512036]
[8]翟俊海,刘博,张素芳.基于粗糙集相对分类信息熵和粒子群优化的特征选择方法[J].智能系统学报,2017,12(3):397.[doi:10.11992/tis.201705004]
ZHAI Junhai,LIU Bo,ZHANG Sufang.A feature selection approach based on rough set relative classification information entropy and particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2017,12():397.[doi:10.11992/tis.201705004]
[9]何明,常盟盟,刘郭洋,等.基于SQL-on-Hadoop查询引擎的日志挖掘及其应用[J].智能系统学报,2017,12(5):717.[doi:10.11992/tis.201706016]
HE Ming,CHANG Mengmeng,LIU Guoyang,et al.Log mining and application based on sql-on-hadoop query engine[J].CAAI Transactions on Intelligent Systems,2017,12():717.[doi:10.11992/tis.201706016]
[10]汪培庄.因素空间理论——机制主义人工智能理论的数学基础[J].智能系统学报,2018,13(1):37.[doi:10.11992/tis.201711034]
WANG Peizhuang.Factor space-mathematical basis of mechanism based artificial intelligence theory[J].CAAI Transactions on Intelligent Systems,2018,13():37.[doi:10.11992/tis.201711034]
备注/Memo
收稿日期：2006-02-15
基金项目：国家自然科学基金资助项目(60573075).
作者简介：
张继福，男，教授，2005年毕业于北京理工大学，获工学博士学位，CCF高级会员，主要研究方向：数据仓库与数据挖掘、人工智能及应用.已发表学术论文50余篇，其中被SCI、EI 收录20余篇.Email:jifuzh@sina.com.
张素兰，女，副教授，2003年毕业于太原科技大学，获工学硕士学位，主要研究方向：概念格与数据挖掘.已发表学术论文10余篇.
胡立华，女，助教，2006年毕业于太原科技大学，获工学硕士学位，主要研究方向：概念格与数据挖掘.已发表学术论文3篇.?
更新日期/Last Update:
2009-04-27