IDEAS home Printed from https://ideas.repec.org/a/bpj/jossai/v9y2021i1p95-104n6.html
   My bibliography  Save this article

The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm

Author

Listed:
  • Shao Fubo

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing100044, China)

  • Liu Hui

    (School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao266061, China)

Abstract

In the era of big data, correlation analysis is significant because it can quickly detect the correlation between factors. And then, it has been received much attention. Due to the good properties of generality and equitability of the maximal information coefficient (MIC), MIC is a hotspot in the research of correlation analysis. However, if the original approximate algorithm of MIC is directly applied into mining correlations in big data, the computation time is very long. Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n2.4 when parameters are default. And the experiments show that the large number of candidate partitions of random relationships results in long computation time. The analysis is a good preparation for the next step work of designing new fast algorithms.

Suggested Citation

  • Shao Fubo & Liu Hui, 2021. "The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 9(1), pages 95-104, February.
    • Handle: RePEc:bpj:jossai:v:9:y:2021:i:1:p:95-104:n:6
    DOI: 10.21078/JSSI-2021-095-10
    as

    Download full text from publisher

    File URL: https://doi.org/10.21078/JSSI-2021-095-10
    Download Restriction: no
    File URL: https://libkey.io/10.21078/JSSI-2021-095-10?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
    ---><---

    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:bpj:jossai:v:9:y:2021:i:1:p:95-104:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.