IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/802505.html
   My bibliography  Save this article

Research on FCM and NHL Based High Order Mining Driven by Big Data

Author

Listed:
  • Zhen Peng
  • Jie Peng
  • Wei Zhao
  • Zhenguo Chen

Abstract

In order to get the high order evaluation and correlation degree among big data with the characteristics of multidimension and multigranularity, an FCM and NHL based high order mining algorithm driven by big data is proposed, which is a kind of machine learning based on qualitative knowledge. The algorithm is applied in scientific and technical talent forecast. Driven by the big data of scientific research track of scientific and technical talents, the index system is designed and the big data is automatically acquired and processed. Accordingly, the high order evaluations in dimension level and target level can be inferred by the correlation weights mining. And the outstanding young talents in material field in 2014 have been actively recommended to review department for decision-making.

Suggested Citation

  • Zhen Peng & Jie Peng & Wei Zhao & Zhenguo Chen, 2015. "Research on FCM and NHL Based High Order Mining Driven by Big Data," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-7, June.
    • Handle: RePEc:hin:jnlmpe:802505
    DOI: 10.1155/2015/802505
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2015/802505.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2015/802505.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2015/802505?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:802505. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.