An Exploratory Study and Application of Data Mining: Railway Alarm Data

  • Yichuan Yang
  • Hanning YuanEmail author
  • Dapeng Li
  • Tianyun Shi
  • Wen Cheng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)


The railway industry generates large data but there are few researches on railway data analysis. The paper presented an exploratory study and application of data mining from railway alarm data. The railway alarm data is analyzed to find the correlation between alarm items and between railway bureaus when alarm occurred and predict the alarm occurring. The paper proposed an alternative measurement mode with three values: support, Kulc and balance to mine the correlation from alarm data analysis, and the results finally indicated the very possibility of associated railway bureaus.


Data mining Association rules Railway alarm data 



This work was supported by National Key Research and Development Plan of China (2016YFB0502604, 2016YFC0803000), International Scientific and Technological Cooperation and Academic Exchange Program of Beijing Institute of Technology (GZ2016085103), Frontier and interdisciplinary innovation program of Beijing Institute of Technology (2016CX11006).


  1. 1.
    Wang, S., Yuan, H.: Spatial data mining: a perspective of big data. J. Int. J. Data Warehous. Min. 10, 50–70 (2014)CrossRefGoogle Scholar
  2. 2.
    Li, D., Wang, S., Yuan, H.: Software and applications of spatial data mining. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 06, 84–144 (2016)CrossRefGoogle Scholar
  3. 3.
    Wu, X., Zhu, X., Gongqing, W., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26, 97–107 (2014)CrossRefGoogle Scholar
  4. 4.
    Adedoyin-Olowe, M., Gaber, M.M., Dancausa, C.M., Stahl, F., Gomes, J.B.: A rule dynamics approach to event detection in twitter with its application to sports and politics. Expert Syst. Appl. 55, 351–360 (2016)CrossRefGoogle Scholar
  5. 5.
    Khader, N., Lashier, A., Yoon, S.W.: Pharmacy robotic dispensing and planogram analysis using association rule mining with prescription data. Expert Syst. Appl. 57, 296–310 (2016)CrossRefGoogle Scholar
  6. 6.
    Kim, J., Han, M., Lee, Y., Park, Y.: Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Systems with Applications 57, 31–324 (2016)CrossRefGoogle Scholar
  7. 7.
    Li, L., Lu, R., Choo, K.-K.R., Datta, A., Shao, J.: Privacy-preserving-outsourced association rule mining on vertically partitioned databases. IEEE Trans. Inf. Forensics Secur. 11, 1847–1861 (2016)CrossRefGoogle Scholar
  8. 8.
    Martin, D., Alcala-Fdez, J., Rosete, A., Herrera, F.: NICGAR: a niching genetic algorithm to mine a diverse set of interesting quantitative association rules. Inf. Sci. 355, 208–228 (2016)CrossRefGoogle Scholar
  9. 9.
    Parkinson, S., Somaraki, V., Ward, R.: Auditing file system permissions using association rule mining. Expert Syst. Appl. 55, 27–283 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yichuan Yang
    • 1
  • Hanning Yuan
    • 1
    Email author
  • Dapeng Li
    • 1
  • Tianyun Shi
    • 2
  • Wen Cheng
    • 3
  1. 1.International School of SoftwareBeijing Institute of TechnologyBeijingChina
  2. 2.Institute of Computing TechnologiesChina Academy of Railway SciencesBeijingChina
  3. 3.School of Aerospace EngineeringBeijing Institute of TechnologyBeijingChina

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