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

Shao Fubo () and Liu Hui ()
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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

Journal of Systems Science and Information, 2021, vol. 9, issue 1, 95-104

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.

Keywords: statistical correlation; the maximal information coefficient; approximate algorithm; mutual information; dynamic programming algorithm (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:9:y:2021:i:1:p:95-104:n:6

DOI: 10.21078/JSSI-2021-095-10

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