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Data Mining Approach in Scientific Research Organizations Evaluation Via Clustering

  • Conference paper
Data Mining and Knowledge Management (CASDMKM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3327))

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Abstract

Data mining is a useful tool to draw useful information from large database. In scientific research organizations evaluation, there exists a problem of using the same criteria to evaluate different types of research organizations. In this paper we propose a clustering method to make classification of the scientific research organization of CAS, and then according to this classification we evaluate the scientific research organization using the annual evaluation database of CAS to test our method.

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Author information

Authors and Affiliations

  1. University of Science & Technology of China, Hefei, 230026, China

    Jingli Liu

  2. Institute of Policy and Management, Chinese Academy of Sciences, Beijing, 100080, China

    Jingli Liu, Jianping Li & Weixuan Xu

  3. Graduate School of Chinese Academy of Sciences, Beijing, 100039, China

    Yong Shi

Authors
  1. Jingli Liu
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  2. Jianping Li
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  3. Weixuan Xu
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  4. Yong Shi
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Editor information

Editors and Affiliations

  1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China; College of Information Science and Technology, University of Nebraska at Omaha, 68182, Omaha, NE, USA

    Yong Shi

  2. Institute of Policy & Management, Chinese Academy of Sciences, P.O. Box, 100080, Beijing, P.R. China

    Weixuan Xu

  3. College of Information Science and Technology, University of Nebraska at Omaha, 68182, NE, USA

    Zhengxin Chen

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© 2004 Springer-Verlag Berlin Heidelberg

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Liu, J., Li, J., Xu, W., Shi, Y. (2004). Data Mining Approach in Scientific Research Organizations Evaluation Via Clustering. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_14

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  • DOI: https://doi.org/10.1007/978-3-540-30537-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23987-1

  • Online ISBN: 978-3-540-30537-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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