Advertisement

Cluster Computing

, Volume 22, Supplement 2, pp 4551–4559 | Cite as

Factors correlation mining on maritime accidents database using association rule learning algorithm

  • Huang ChanghaiEmail author
  • Hu Shenping
Article

Abstract

Maritime safety is of paramount significance for marine industry since the maritime accidents may adversely affect the human, cargos, ships and the marine environment in various forms and degree of extent. The study aims to identify potential causal relationships among the many factors that play a role in maritime accidents. Correspondingly, association rule learning is selected as analysis approach, because of its utility in obtaining association rules through data mining on maritime accidents data. Based on the analysis of association rule learning, this study designs the association rules learning procedure of maritime accidents and establishes the association rule learning model of maritime accidents. The novelty of this study is to present a different perspective during maritime accident analysis in which potential causal relationships among the many factors are revealed. Association rule learning of maritime accidents data is carried out based on the Apriori algorithm, and the strong association rules among the causal factors of the accident are generated. The study then analyzed the generated strong association rules to find the potential relationship among the causal factors, and puts forward the coping strategies to prevent similar maritime accidents occurrence.

Keywords

Maritime accident Data mining Apriori algorithm Association rules learning Marine traffic safety 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers and editors for their comments and suggestions. The research is supported by the China Postdoctoral Science Foundation (2016M591651), the Creative Activity Plan for Science and Technology Commission of Shanghai (13510501600, 16040501700), the Innovation Foundation of SMU for Ph.D. Graduates (yc2012067), and the Fostering Foundation for the Excellent Ph.D. Dissertation of SMU (2013bxlp006).

References

  1. 1.
    Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inf. 6(3), 724–730 (2016)Google Scholar
  2. 2.
    Mullai, A., Paulsson, U.: A grounded theory model for analysis of marine accidents. Accid. Anal. Prev. 43(4), 1590–1603 (2011)Google Scholar
  3. 3.
    Ellis, J.: Analysis of accidents and incidents occurring during transport of packaged dangerous goods by sea. Saf. Sci. 49(8), 1231–1237 (2011)Google Scholar
  4. 4.
    Eleftheria, E., Apostolos, P., Markos, V.: Statistical analysis of ship accidents and review of safety level. Saf. Sci. 85, 282–292 (2016)Google Scholar
  5. 5.
    Akyuz, E.: A hybrid accident analysis method to assess potential navigational contingencies: the case of ship grounding. Saf. Sci. 79, 268–276 (2015)Google Scholar
  6. 6.
    Arunkumar, N., Ram Kumar, K., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, Tsallis entropy and Kolmogorov complexity. J. Med. Imaging Health Inf. 6(2), 526–531 (2016)Google Scholar
  7. 7.
    Akyuz, E., Celik, M.: Utilisation of cognitive map in modelling human error in marine accident analysis and prevention. Saf. Sci. 70, 19–28 (2014)Google Scholar
  8. 8.
    Akyuz, E.: A marine accident analysing model to evaluate potential operational causes in cargo ships. Saf. Sci. 92, 17–25 (2017)Google Scholar
  9. 9.
    Park, J.W., Jeong, J.S., Park, G.K.: An effect of traffic speed on maritime accidents. Soft Computing in Intelligent Control. Springer International Publishing, pp. 29–43 (2014)Google Scholar
  10. 10.
    Bulut, E., Yoshida, S.: Are marine accident really accident? Fallacy of random marine accidents in dry cargo fleet. Asian J. Shipping Logist. 31(2), 217–229 (2015)Google Scholar
  11. 11.
    Huang, D.Z., Hu, H., Li, Y.Z.: Spatial analysis of maritime accidents using the geographic information system. Transp. Res. Record: J. Transp. Res. Board 2326, 39–44 (2013)Google Scholar
  12. 12.
    Alsolami, F., Amin, T., Chikalov, I., et al.: Dynamic programming approach for construction of association rule systems. Fundamenta Informaticae 147(2–3), 159–171 (2016)Google Scholar
  13. 13.
    Jaiswal, V., Agarwal, J.: The evolution of the association rules. Int. J. Model. Optim. 2(6), 726 (2012)Google Scholar
  14. 14.
    Saabith, A.L.S., Sundararajan, E., Bakar, A.A.: Parallel implementation of Apriori algorithms on the Hadoop-MapReduce platform—an evaluation of literature. J. Theor. Appl. Inf. Technol. (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Merchant Marine CollegeShanghai Maritime UniversityShanghaiChina

Personalised recommendations