• DocumentCode
    3462944
  • Title

    Trend Analysis of Machine Learning - A Text Mining And Document Clustering Methodology

  • Author

    Yang, Jiann-Min ; Liao, Wei-Cheng ; Wu, Wen-Chin ; Yin, Chi-Yen

  • Author_Institution
    Dept. of Manage. Inf. Syst., Nat. Chengchi Univ., Taipei, Taiwan
  • fYear
    2009
  • fDate
    June 30 2009-July 2 2009
  • Firstpage
    481
  • Lastpage
    486
  • Abstract
    The machine learning is certificated as one of the most important technologies in todaypsilas world. There are several various researches applying machine learning to improve its operation efficiency in many different aspects.Based on the social science citation index (SSCI) database,this research is using text mining technology which collecting the homogeneous glossaries in the articles, conducting to the literature cluster analysis. To select the term frequency index which generated by various glossaries aggregation from each article as well as an input variable for self-organization map(SOM) network, following by utilizing the network neuron automatic clustering function, dividing into 10 application domains of machine learning, finally proceeding the trend analysis coordinated with the articles by published year,discovering the historical vein and collecting the results by each research area, and further forecasting the future possible tendency.
  • Keywords
    data mining; learning (artificial intelligence); neural nets; pattern clustering; self-organising feature maps; text analysis; document clustering; machine learning; network neuron automatic clustering function; self-organization map network; social science citation index; term frequency index; text mining; trend analysis; Citation analysis; Databases; Frequency conversion; Indexes; Input variables; Machine learning; Neurons; Terminology; Text mining; Veins; document clustering; neural network; text mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    New Trends in Information and Service Science, 2009. NISS '09. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-3687-3
  • Type

    conf

  • DOI
    10.1109/NISS.2009.176
  • Filename
    5260843