• DocumentCode
    571428
  • Title

    Designing China´s Local Government Performance Evaluation Indicators Based on Data Mining: An Exploratory Study in Four Municipal Governments in Jiangsu

  • Author

    Huping, Shang ; Chunhua, Hui

  • Author_Institution
    Manage. Sch., Lanzhou Univ., Lanzhou, China
  • fYear
    2012
  • fDate
    18-21 Aug. 2012
  • Firstpage
    486
  • Lastpage
    494
  • Abstract
    In a tremendously large country like China, there are substantial differences on natural geographic features, history backgrounds, and social realities between the varied regions. Because of the heterogeneity, the local government performance evaluation indicator designing is a systematical engineering which could be affected by various factors. In order to solve the problems, we established a series of local government-oriented performance evaluation indicators database, data marts and data warehouses. On the basis of the data warehouse, this study uses RBF neural network as the soft clustering tool to excavate 10 stair indicators, 30 secondary indicators and 90 triple indicators under some support degrees.
  • Keywords
    data mining; data warehouses; government data processing; performance evaluation; radial basis function networks; China local government performance evaluation indicators; Jiangsu; RBF neural network; data marts; data mining; data warehouses; database; government-oriented performance evaluation; history backgrounds; natural geographic features; social realities; systematical engineering; Clustering algorithms; Data mining; Data warehouses; Databases; Government; Neural networks; Performance evaluation; data warehouse; government performance; ndicators; the 4 municipal governments;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Business Intelligence and Financial Engineering (BIFE), 2012 Fifth International Conference on
  • Conference_Location
    Lanzhou
  • Print_ISBN
    978-1-4673-2092-4
  • Type

    conf

  • DOI
    10.1109/BIFE.2012.109
  • Filename
    6305172