• DocumentCode
    3228696
  • Title

    Finding Short Patterns to Classify Text Documents

  • Author

    An, Jiyuan ; Chen, Yi-Ping Phoebe

  • Author_Institution
    Sch. of Inf. Technol., Deakin Univ., Geelong, Vic.
  • fYear
    2006
  • fDate
    18-22 Dec. 2006
  • Firstpage
    293
  • Lastpage
    296
  • Abstract
    Many classification methods have been proposed to find patterns in text documents. However, according to Occam\´s razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm
  • Keywords
    classification; text analysis; tree searching; Occam razor principle; breadth-first algorithm; depth-first pattern generation algorithm; text document classification; text document pattern finding; Australia; Equations; Information technology; Robustness; Support vector machine classification; Support vector machines; Test pattern generators; Testing; Text categorization; Document Categorization; breadth-first; depth-first.; rule generation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    0-7695-2747-7
  • Type

    conf

  • DOI
    10.1109/WI.2006.82
  • Filename
    4061379