Affiliations: [a] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China | [b] Beijing Laboratory of Urban Rail Transit, Beijing Jiaotong University, Beijing, China
Correspondence:
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Corresponding author: Keping Li, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China. E-mail:kpli@bjtu.edu.cn
Abstract: The maximal information coefficient (MIC), a measure of dependence for two-variable relationships, can be used to discover the relationships between two variables in big data. This paper proposes a new mathematical program model for calculating the value of MIC. A corresponding efficient algorithm is designed to solve the model in big data environment. In order to illustrate the validity of the proposed algorithm, the proposed algorithm is applied into the analysis of railway accidents data. Experimental results show that the proposed algorithm could find important relationships between two variables from big data. And some factors influencing accidents are identified from many factors. In addition, compared with the algorithm proposed by Reshef et al. in 2011, the proposed algorithm has lower time complexity and needs less computation time. Hence the proposed algorithm is more suitable for big data environment.
Keywords: Maximal information coefficient, k-means clustering algorithm, big data, railway accidents analysis
