• DocumentCode
    1362422
  • Title

    Effective Pattern Discovery for Text Mining

  • Author

    Zhong, Ning ; Li, Yuefeng ; Wu, Sheng-Tang

  • Author_Institution
    Dept. of Life Sci. & Inf., Maebashi Inst. of Technol., Maebashi, Japan
  • Volume
    24
  • Issue
    1
  • fYear
    2012
  • Firstpage
    30
  • Lastpage
    44
  • Abstract
    Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.
  • Keywords
    data mining; pattern recognition; text analysis; data mining; pattern discovery; polysemy; synonymy; text documents; text mining; Computational modeling; Electronic mail; Filtering algorithms; Information analysis; Noise measurement; Pattern recognition; Text mining; Text mining; information filtering.; pattern evolving; pattern mining; text classification;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/TKDE.2010.211
  • Filename
    5611523