IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v13y2017i1p76-93.html
   My bibliography  Save this article

Improved Approximation Algorithm for Maximal Information Coefficient

Author

Listed:
  • Shuliang Wang

    (School of Software, Beijing Institute of Technology, Beijing, China)

  • Yiping Zhao

    (Software Center, Bank of China, Beijing, China)

  • Yue Shu

    (Tencent Technology (Beijing) Company Limited, Beijing, China)

  • Wenzhong Shi

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

Abstract

A novel statistical maximal information coefficient (MIC) that can detect the nonlinear relationships in large data sets was proposed by Reshef et al. (2011), with emphasis being placed on the equitability, which is a very important concept in data exploration. In this paper, an improved algorithm for approximation of the MIC (IAMIC) is proposed for the development of the equitability. Based on quadratic optimization processes, the IAMIC can search for a more optimal partition on the y-axis rather than use that which was obtained simply through the equipartition of the y-axis, to enable it to come closer to the true value of the MIC. It has been proved that the IAMIC can search for a local optimal value while using a lower number of iterations. It has also been shown that the IAMIC provides higher accuracy and a more acceptable run-time, based on both a mathematical proof and the results of simulations.

Suggested Citation

  • Shuliang Wang & Yiping Zhao & Yue Shu & Wenzhong Shi, 2017. "Improved Approximation Algorithm for Maximal Information Coefficient," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 13(1), pages 76-93, January.
    • Handle: RePEc:igg:jdwm00:v:13:y:2017:i:1:p:76-93
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.2017010104
    Download Restriction: no
    ---><---

    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:igg:jdwm00:v:13:y:2017:i:1:p:76-93. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.