A Study on Classification Method of Discrete Data Basic on Improved Association Rules

  • YueQin Cao
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 169)


A large amount of data is involved in the processing of discrete data. As there is a large quantity of redundancy discrete data in mass data, it causes the error of discrete data association and reduced classification effect. To solve this problem, we brought forward the classification method of discrete data to improve associated mining algorithm. Before establishing association rule, we performed secondary validation of information which may contain redundancy data to lower down the probability of error in discrete data association, thus the possibility of false classification of data, and remove the defect of traditional method. As proved by simulation experiment, this improved algorithm is able to largely raise the preciseness of discrete data association and achieve better effect.


data classification association rule improved algorithm 


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  1. 1.
    Bray, T., Paoli, J., Sperberg-McQueen, C.M.: Extensible Markup Language(XML)1.0, 2nd edn., pp. 21–24. W3C Recommendation (October 2000)Google Scholar
  2. 2.
    Abiteboul, S., Bnueman, P., Suciu, D.: Data on the Web, pp. 89–93. Morgan Kaufmann Publishers (2000)Google Scholar
  3. 3.
    Xuan, W.C., Ping, L.X.: XML Database Technology, pp. 137–200. Tsinghua University Press, Beijing (2008)Google Scholar
  4. 4.
    Zhong, S.Y.: XML Theory and Application Basic, pp. 130–134. Press of Beijing University of Posts and Telecommunications, Beijing (2000)Google Scholar
  5. 5.
    Wen, T.J.: Design and Embodiment of A Kind of Distributive Web Log Mining System. Computer Simulation 10(109) (2006)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Computer DepartmentWenzhou Vocational and Technical CollegeWenzhouCityChina

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