Development of a Job Stress Evaluation Methodology Using Data Mining and RSM

  • Yonghee Lee
  • Sangmun Shin
  • Yongsun Choi
  • Sang Do Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4541)


Data mining (DM) has emerged as one of the key features of many applications on information system. While a number of data computing and analyzing method to conduct a survey analysis for a job stress evaluation represent a significant advance in the type of analytical tools currently available, there are limitations to its capability such as dimensionality associated with many survey questions and quality of information. In order to address these limitations on the capabilities of data computing and analyzing methods, we propose an advanced survey analysis procedure incorporating DM into a statistical analysis, which can reduce dimensionality of the large data set, and which may provide detailed statistical relationships among the factors and interesting responses by utilizing response surface methodology (RSM). The primary objective of this paper is to show how DM techniques can be effectively applied into a survey analysis related to a job stress evaluation by applying a correlation-based feature selection (CBFS) method. This CBFS method can evaluate the worth of a subset including input factors by considering the individual predictive ability of each factor along with the degree of redundancy between pairs of input factors. Our numerical example clearly shows that the proposed procedure can efficiently find significant factors related to the interesting response by reducing dimensionality.


Job stress evaluation Survey analysis Data mining  Correlation-based feature selection (CBFS) Response surface methodology (RSM) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Frawley, W., Piatetsky-Shapiro, G., Matheus, C.: Knowledge Discovery in Databases: An Overview. AI Magazine, Fall, pp. 213–228 (1992) Google Scholar
  2. 2.
    Yu, L., Liu, H.: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: The Proceedings of the 20th International Conference on Machine Leaning (ICML-03), Washington, DC, pp. 856–863 (2003)Google Scholar
  3. 3.
    Allen, D.: The Relationship between Variable Selection and Data Augmentation and a Method for Prediction. Technometrics 16, 125–127 (1974)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C. Cambridge University Press, Cambridge (1988)zbMATHGoogle Scholar
  5. 5.
    Quinlan, R.R.: Induction of Decision Trees. Machine Learning, vol. 1(1), Hingham, MA, pp. 81–106 (1986) Google Scholar
  6. 6.
    Gardner, M., Bieker, J.: Data Mining Solves Tough Semiconductor Manufacturing Problems. In: Conference on Knowledge Discovery in Data Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 376–383. ACM Press, New York (2000)CrossRefGoogle Scholar
  7. 7.
    Witten, I.W.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  8. 8.
    Su, C.T., Chen, M.C., Chan, H.L: Applying Neural Network and Scatter Search to Optimize Parameter Design with Dynamic Characteristics. Journal of the Operational Research Society 56, 1132–1140 (2005)zbMATHCrossRefGoogle Scholar
  9. 9.
    Yi, G., Choi, M.G., Choi, Y.S., Shin, S.M.: Development of a Data Mining Methodology Using Robust Design. WSEAS Transactions on Computers 5(5), 852–857 (2006)Google Scholar
  10. 10.
    Seifert, J.W.: Data Mining: An Overview. CRS Report RL31798 (2004)Google Scholar
  11. 11.
    Lin, G., Ash, G., Doran, C., Kevin, H.: Macro-ergonomic Risk Assessment in Nuclear Remediation Industry. Applied Ergonomics, pp. 241–254 (1996)Google Scholar
  12. 12.
    O’Neill Michael, J.: Ergonomic Design for Organizational Effectiveness. Lewis Publishers (1998)Google Scholar
  13. 13.
    National Research Council and the Institute of Medicine: Musculoskeletal Disorders and the Workplace: Low Back and Upper Extremities. Panel on Musculoskeletal Disorders and the Workplace. Commission on Behavioral and Social Sciences and Education. National Academy Press, Washington, DC (2001)Google Scholar
  14. 14.
    Deveraux, J.J., Vlachonikolis, I.G., Buckle, P.W.: Epidemiological Study to Investigate Potential Interaction between Physical and Psychosocial Factors at Work that may Increase the Risk of Symptoms of Musculoskeletal Disorder of the Neck and Upper Limb. Occupational and Environmental Medicine 59(4), 269–277 (2002)CrossRefGoogle Scholar
  15. 15.
    Karasek, R., Gordon, G., Pietrokovsky, C., Rrese, M., Pieper, C., Schwartz, J., Fry, L., Schirer, D.: Job Content Questionnaire: Questionnaire and User’s Guide. Lowell, University of Massachusetts (1985)Google Scholar
  16. 16.
    Hurrell, J.J., McLaney, M.A.: Exposure to Job Stress – a New Psychometric Instrument. Scand. J. Work Environ. Health 14, 27–28 (1988)Google Scholar
  17. 17.
    Hall, M.A.: Correlation-based Feature Selection for Machine Learning. Ph.D diss. Waikato University. Department of Computer Science. Hamilton, New Zealand (1998)Google Scholar
  18. 18.
    Xu, Q., Kamel, M., Salama, M.M.A.: Significance Test for Feature Subset Selection on Image Recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 244–252. Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Langley, P.: Selection of Relevant Features in Machine Learning. In: Proceedings of the AAAI Fall Symposium on Relevance, pp. 140–144. AAAI Press, Stanford (1994)Google Scholar
  20. 20.
    Box, G.E.P., Bisgaard, S., Fung, C.: An Explanation and Critique of Taguchi’s Contributions to Quality Engineering. International Journal of Reliability Management 4, 123–131 (1988)Google Scholar
  21. 21.
    Shin, S., Cho, B.R.: Bias-specified robust design optimization and its analytical solutions. Computer & Industrial Engineering 48, 129–140 (2005)CrossRefGoogle Scholar
  22. 22.
    Merz, C.J. and Murphy, P.M.: UCI Repository of Machine Learning Database,
  23. 23.
    MINITAB: Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yonghee Lee
    • 1
  • Sangmun Shin
    • 2
  • Yongsun Choi
    • 2
  • Sang Do Lee
    • 1
  1. 1.Department of Industrial Management Engineering, Dong-A University, BusanSouth Korea
  2. 2.Department of Systems Management Engineering, Inje University, GimhaeSouth Korea

Personalised recommendations