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Original Articles

Text-document clustering-based cause and effect analysis methodology for steel plant incident data

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Pages 416-426 | Received 08 Mar 2017, Accepted 20 Mar 2018, Published online: 07 Apr 2018

References

  • Aggarwal, C. C., & Zhai, C. (2012). Mining text data. New York, NY: Springer Science & Business Media.
  • Albright, R. (2004). Taming text with the SVD. SAS Institute White Paper. Retrieved from ftp://ftp.sas.com/techsup/download/EMiner/TamingTextwiththeSVD.pdf
  • Albright, R., Bartlett, J., & Bultman, D. (2007). Making web results relevant with SAS. Paper presented at the SAS Global Forum 2007, Orlando, FL.
  • Bailey, K. D. (1989). Taxonomy and disasters: Prospects and problems. International Journal of Mass Emergencies and Disasters, 7(3), 419–431.
  • Brooks, B. (2008). Shifting the focus of strategic occupational injury prevention Mining free-text, workers compensation claims data. Safety Science, 46, 1–21.
  • Chandler, M. D., Bunn, T. L., Slavova, S., Chandler, M. D., Bunn, T. L., & Narrative, S. S. (2017). Narrative and quantitative analyses of workers’ compensation-covered injuries in short-haul vs. long-haul trucking. International Journal of Injury Control and Safety Promotion, 24(1), 120–130.
  • Coussement, K. (2008). Employing SAS® text miner methodology to become a customer genius in customer churn prediction and complaint e-mail management. Paper presented at the SAS Global Forum 2008, Customer Intelligence, San Antonio, TX.
  • Freitas, A. A. (2002). Data mining and knowledge discovery with evolutionary algorithms (1st ed.). Berlin, Heidelberg: Springer-Verlag.
  • Geoffrey, T., Kellie, E., & Roy, H. (2004). Enhancing occupational safety and health. Oxford, UK: Elsevier.
  • Hawkins, D. T., Larson, S. E., & Caton, B. Q. (2003). Information science abstracts: Tracking the literature of information science. Part 2: A new taxonomy for information science. Journal of the American Society for Information Science and Technology, 54(8), 771–781.
  • Ishikawa, K. (1982). Guide to quality control. Tokyo: Asian Productivity Organization.
  • Jayabharathy, J., & Kanmani, S. (2014). Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature. Decision Analytics, 1(1), 3.
  • Kiela, D., Guo, Y., Stenius, U., & Korhonen, A. (2015). Unsupervised discovery of information structure in biomedical documents. Bioinformatics, 31(7), 1084–1092.
  • Kumar, R., & Ghosh, A. K. (2014). The accident analysis of mobile mine machinery in Indian opencast coal mines. International Journal of Injury Control and Safety Promotion, 21(1), 54–60.
  • Landucci, G., Lovicu, G., Barontini, F., Guidi, L., & Nicolella, C. (2014). Safety issues related to the maintenance operations in a vegetable oil refinery: A case study. Journal of Loss Prevention in the Process Industries, 30(1), 95–104.
  • Larsson, T. J., Tezic, K., & Oldertz, C. (2010). Threats and violence as a precursor to occupational injury: Text-mining of insurance-based information on police officers and security guards in Sweden 2004–2007. Safety Science Monitor, 14(2), 1–14.
  • Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368.
  • Lincoln, A. E., Sorock, G. S., Courtney, T. K., Wellman, H. M., Smith, G. S., & Amoroso, P. J. (2004). Using narrative text and coded data to develop hazard scenarios for occupational injury interventions. Injury Prevention, 10(4), 249–254.
  • Manning, C. D., Raghavan, P., & Schütze, H. (2008). An Introduction to Information Retrieval. New York: Cambridge University Press.
  • McKenzie, K., Scott, D. A., Campbell, M. A., & McClure, R. J. (2010). The use of narrative text for injury surveillance research: A systematic review. Accident Analysis and Prevention, 42, 354–363.
  • Rachid, C., Ion, V., Irina, C., & Mohamed, B. (2015). Preserving and improving the safety and health at work: Case of Hamma Bouziane cement plant (Algeria). Safety Science, 76, 145–150.
  • Rajman, M., & Vesely, M. (2004). From text to knowledge: Document processing and visualization: A text mining approach. In S. Sirmakessis (Ed.), Text mining and its applications: Results of the NEMIS launch conference (pp. 7–24). Berlin, Heidelberg: Springer.
  • Robinson, S. D., Irwin, W. J., Kelly, T. K., & Wu, X. O. (2015). Application of machine learning to mapping primary causal factors in self reported safety narratives. Safety Science, 75, 118–129.
  • Sanders, A., & DeVault, C. (2004). Using SAS® at SAS: The mining of SAS technical support. Paper presented at SUGI 29. (pp. 1–22). Montréal, Canada: SAS Institute.
  • Stewart, G. W. (1993). On the early history of the singular value decomposition. SIAM Review, 35, 551–566.
  • Svenson, O., Lekberg, A., & Johansson, A. E. L. (1999). On perspective, expertise and differences in accident analyses: Arguments for a multidisciplinary integrated approach. Ergonomics, 42(11), 1561–1571.
  • Taib, I. A., McIntosh, A. S., Caponecchia, C., & Baysari, M. T. (2012). Comparing the usability and reliability of a generic and a domain-specific medical error taxonomy. Safety Science, 50(9), 1801–1805.
  • Tremblay, M. C., Berndt, D. J., Luther, S. L., Foulis, P. R., & French, D. D. (2009). Identifying fall-related injuries: Text mining the electronic medical record. Information Technology and Management, 10, 253–265.
  • Wallace, B., & Ross, A. (2006). Beyond human error: Taxonomies and safety science. Boca Raton, FL: CRC Press.
  • Weiss, S. M., Indurkhya, N., & Zhang, T. (2010). Fundamentals of predictive text mining. London: Springer.
  • Williamson, A., Feyer, A. M., Stout, N., Driscoll, T., & Usher, H. (2001). Use of narrative analysis for comparisons of the causes of fatal accidents in three countries: New Zealand, Australia, and the United States. Injury Prevention, 7(Suppl I), i15–i20.
  • Yoon, B., & Park, Y. (2004). A text-mining-based patent network: Analytical tool for high-technology trend. Journal of High Technology Management Research, 15(1), 37–50.

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