• DocumentCode
    510176
  • Title

    The Library Evaluation Based on the PCA and Fuzzy-c Means

  • Author

    Wei, Yong ; Li, Huazhong

  • Author_Institution
    Software Eng. Dept., Shenzhen Inst. of Inf. Technol., Shenzhen, China
  • Volume
    2
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    167
  • Lastpage
    171
  • Abstract
    As an important library management method the utility and evolution of the library evaluation is much caused from the progression in library circle, and it fits needs to library´s management. Based on the development of data mining technology, a Fuzzy-C-Means (FCM) algorithm model is founded to do the library evaluation in this paper, Selecting the Principal component analysis (PCA) to reduce the dimensionality of indexes, and then extract principal components to replace the original indexes, thus reducing the dimensions of the sample input space, at a certain extent improved the accuracy and the classify effect of this algorithm. At last, apply this model to library evaluation, and it shows more generalized performance and better regression accuracy compared with the method of single Fuzzy-c means and BP neural networks.
  • Keywords
    academic libraries; data mining; fuzzy set theory; indexing; library automation; principal component analysis; BP neural networks; PCA; data mining technology; fuzzy-c means; library circle progression; library evaluation; library management method; principal component analysis; Algorithm design and analysis; Clustering algorithms; Conference management; Data mining; Fuzzy neural networks; Information analysis; Performance analysis; Principal component analysis; Software libraries; Technology management; Fuzzy-c means; Library evaluation; PCA;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.107
  • Filename
    5376428