• DocumentCode
    263508
  • Title

    Keen-Means: A Web Page Clustering Tool Based on an Self-Adjustable K-Means Algorithm

  • Author

    Chun Hsiung Tseng ; Yung Hui Chen ; Chu Chun Chuang ; Jia Hua Wu ; Yi Syuan Yang ; Ya Wen Liang

  • Author_Institution
    Dept. of Inf. Manage., Nanhua Univ., Chiayi, Taiwan
  • fYear
    2014
  • fDate
    12-14 July 2014
  • Firstpage
    300
  • Lastpage
    304
  • Abstract
    Search engines usually do their jobs well. However, due to the fact that most existing search algorithms are keyword-based, search engines may not work as expected in some scenarios when ambiguity problems are encountered. A possible approach to overcome it is to categorize Web resources in advance. In this research, a k-means variation, the keen-means algorithm, along with its implementation is proposed. The algorithm will dynamically and automatically adjust the k value to achieve better results.
  • Keywords
    Internet; pattern clustering; search engines; Web page clustering tool; Web resources; k-means variation; keen-means algorithm; keyword-based search engines; search algorithms; self-adjustable k-means algorithm; Algorithm design and analysis; Clustering algorithms; Google; Labeling; Search engines; Web pages; Web services; Web information extraction; clustering; semantic Web;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Ubi-Media Computing and Workshops (UMEDIA), 2014 7th International Conference on
  • Conference_Location
    Ulaanbaatar
  • Print_ISBN
    978-1-4799-4267-1
  • Type

    conf

  • DOI
    10.1109/U-MEDIA.2014.44
  • Filename
    6916373