• DocumentCode
    424104
  • Title

    The analysis on model of association rules mining based on concept lattice and Apriori algorithm

  • Author

    Hu, Xue-Gang ; Wang, De-Xing ; Liu, Xiao-Ping ; Guo, Un ; Wang, Hao

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Hefei Univ. of Technol., China
  • Volume
    3
  • fYear
    2004
  • fDate
    26-29 Aug. 2004
  • Firstpage
    1620
  • Abstract
    Concept lattice represents knowledge with the relationships between the intension and the extension of concepts, and the relationship between the generalization and the specialization of concepts, thus it is properly applied to the description of association rules mining in database. The quantitative extended concept lattice (QECL) evolves from concept lattice by introducing equivalent relationships to its intension and quantity to its extension, based on it, we can mine association rules, compared with the well-known Apriori algorithm, without calculating frequent itemsets, easily obtain interesting association rules, in the meantime a lot of redundant rules are reduced, thus the efficiency and veracity of the mining rules are improved.
  • Keywords
    data mining; knowledge representation; set theory; Apriori algorithm; association rules mining; knowledge representation; quantitative extended concept lattice; set theory; Algorithm design and analysis; Association rules; Computer science; Data mining; Databases; Itemsets; Iterative algorithms; Lattices; Machine learning algorithms; Nuclear and plasma sciences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
  • Print_ISBN
    0-7803-8403-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2004.1382034
  • Filename
    1382034