The research on model of mining association rules based on quantitative extended concept lattice

被引:0
|
作者
Wang, DX [1 ]
Hu, XG [1 ]
Wang, H [1 ]
机构
[1] Hefei Univ Technol, Dept Comp Sci & Technol, Hefei 230009, Peoples R China
关键词
data mining; association rules; concept lattice; frequent item sets;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Concept Lattice represents knowledge with the relationships between the intension and the extension of concepts, and the relationships between the generalization and the specialization of concepts, thus it is properly applied to the description of mining association rules in databases. The Quantitative Extended Concept Lattice (QECL) evolves from concept lattice by introducing equivalent relationship to its intension and quantity to its extension, which further enriches the relationships between its intensions. Based on QECL, we can mine association rules, comparing with well-known Apriori, Mining association rules on QECL does not need to scan databases for many times, has higher quality of time complexity and shows association rules on the Hasse diagram of QECL more visual and concise, moreover, it can be used to mine association rules interactively according to user's subjective interest.
引用
收藏
页码:134 / 138
页数:5
相关论文
共 50 条
  • [41] RESEARCH OF DATA MINING ALGORITHM BASED ON ASSOCIATION RULES
    Song, Changxin
    Ma, Ke
    PROCEEDINGS OF THE 2011 3RD INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATION (ICFCC 2011), 2011, : 243 - +
  • [42] The Research of Association Rules Mining Algorithm Based on Binary
    Fang, Gang
    Wei, Zu-Kuan
    Yin, Qian
    2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2008, : 879 - +
  • [43] Quantitative and ordinal association rules mining (QAR mining)
    Karel, Filip
    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS, 2006, 4251 : 195 - 202
  • [44] Extended Symbolic Mining of Textures with Association Rules
    Kononenko, Igor
    Bevk, Matjaz
    INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS, 2009, 33 (04): : 487 - 497
  • [45] Fuzzy association rules and the extended mining algorithms
    Chen, GQ
    Wei, Q
    INFORMATION SCIENCES, 2002, 147 (1-4) : 201 - 228
  • [46] A lattice-based approach for mining high utility association rules
    Thang Mai
    Bay Vo
    Nguyen, Loan T. T.
    INFORMATION SCIENCES, 2017, 399 : 81 - 97
  • [47] A lattice-based approach for mining most generalization association rules
    Bay Vo
    Hong, Tzung-Pei
    Le, Bac
    KNOWLEDGE-BASED SYSTEMS, 2013, 45 : 20 - 30
  • [48] Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining
    Shao, Zengzhen
    Li, Yancong
    Wang, Xiao
    Zhao, Xuechen
    Guo, Yanhui
    JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (02) : 539 - 551
  • [49] Concept-guided association rules mining
    Cheng, Jihua
    Shi, Pengfei
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 1999, 36 (09): : 1092 - 1096
  • [50] Research On Novel Model of Data Mining Based on Improved Association Rules and Clustering Algorithm
    Tan, Qing
    PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT, COMPUTER AND SOCIETY (EMCS 2017), 2017, 61 : 522 - 526