Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65892
Title: Improved approximation algorithm for maximal information coefficient
Authors: Wang, S
Zhao, Y
Shu, Y
Shi, W
Keywords: Accuracy
Big data
Equitability
MIC
Quadratic optimization
Issue Date: 2017
Publisher: IGI Global
Source: International journal of data warehousing and mining, 2017, v. 13, no. 1, p. 76-93 How to cite?
Journal: International journal of data warehousing and mining 
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.
URI: http://hdl.handle.net/10397/65892
ISSN: 1548-3924
EISSN: 1548-3932
DOI: 10.4018/IJDWM.2017010104
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