Application of Data Mining in Coal Mine Safety Decision System Based on Rough Set

  • Tianpei Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)


The coal mine safety decision systems, such as ventilation safety monitoring system, underground water inrush monitoring system, underground coal and gas emission monitoring system, have been established in many large and medium-sized coal mines. A large amount of original data had accumulated in these systems. How to transform data into information for scientific decision was a problem worth to consider for coal mine safety production. The rough set theory, quantitative analysis of incomplete, imprecise and uncertainty knowledge, provided a new method and tool for data mining. A kind of heuristic genetic algorithm for continuous attributes discretization was put forward to solve the problem of continuous attribute discretization of decision table; a kind of heuristic immune algorithm for attribute reduction was presented to conquer the shortage of existing attribute reduction algorithm; in order to solve the problem of reasoning and decision in incomplete and imprecise information, a kind of default rule mining model based on reduction lattice was proposed. Finally, data mining system based on rough set was designed, which was applied to data mining analysis of underground gas emission, good results were achieved.


rough set data mining attribute reduction genetic algorithm Immure algorithm reduction lattice default rule 


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  1. 1.
    Niu, L.D.: Mine Gas Linkage Monitoring Method Based on Data Mining Approach. China Safety Science Journal 21, 62–68 (2011)Google Scholar
  2. 2.
    Ni, L.Q., Zhou, H.T., Gao, S.S.: A More Effective Data Mining Approach That Adroitly Combines Rough Set Theory with Evidence Theory. Journal of Northwestern Polytechnical University 28, 927–931 (2010)Google Scholar
  3. 3.
    Zhao, Z.P., Yin, Z.M., Chen, J.C.: Mine Hidden Danger Data Digging Model and Applicative Digging Algorithm. Coal Science and Technology 38, 67–69 (2010)Google Scholar
  4. 4.
    Zheng, H.Z., Liu, Y., Zhan, D.C.: Default Rules Frame of Non-monotonous Problems Based on Data Mining. Computer Science 33, 181–182 (2006)Google Scholar
  5. 5.
    Wang, Y.Y.: Knowledge Discovery Methods Research Based on Rough Set Theory. Shanghai Jiao Tong University, Shanghai (2006)Google Scholar
  6. 6.
    Xu, X., Zhai, J.M.: Multiscale Genetic Algorithms for Discretization in Rough Set on Trees. Modern Manufacturing Engineering 10, 1–4 (2009)Google Scholar
  7. 7.
    Liu, Y., Li, W.H., Chen, Y.L.: Research on Intelligent Fault Diagnosis Based on Artificial Immune System. Computer Measurement & Control 18, 2694–2696 (2010)Google Scholar
  8. 8.
    Zhao, L.S., Shi, J.H.: Real Value Attribute Reduction Method Based on Rough Sets. Journal of Inner Mongolia University 41, 97–101 (2010)Google Scholar
  9. 9.
    Liu, B., Pan, J.H., Liu, P.S.: Rule Evaluation Method and Data Quality Mining System. Computer Integrated Manufacturing Systems 15, 1436–1441 (2009)MathSciNetGoogle Scholar
  10. 10.
    Hou, G.Z.: Focus on Gas Safety Management Face Ventilation and Gas Management Means. Coal Technology 28, 199–200 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tianpei Zhou
    • 1
  1. 1.Xuzhou College of Industrial and TechnologyXuzhouChina

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