Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Data-Driven Activity Monitoring Method for Abnormal Sales Behavior Detection

이상 판매활동을 탐지하기 위한 데이터 기반 활동 모니터링 기법

  • Park, Sungho (School of Industrial Management Engineering, Korea University) ;
  • Kim, Seoung Bum (School of Industrial Management Engineering, Korea University)
  • 박성호 (고려대학교 산업경영공학과) ;
  • 김성범 (고려대학교 산업경영공학과)
  • Received : 2013.12.31
  • Accepted : 2014.06.17
  • Published : 2014.10.15
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Abstract

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.

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References

  1. Barbara, D., Wu, N., and Jajodia, S. (2001), Detecting novel network intrusions using bayes estimators, In First SIAM Conference on Data Mining.
  2. Benneyan, J. C. (1998), Statistical quality control methods in infection control and hospital epidemiology, Part II : chart use, statistical properties, and research issues, Infection Control and Hospital Epidemiology, 265-283.
  3. Chandola, V., Banerjee, A., and Kumar, V. (2009), Anomaly detection : A survey, ACM Computing Surveys (CSUR), 41(3), 15.
  4. Fawcett, T. and Provost, F. (1997), Adaptive fraud detection, Data mining and knowledge discovery, 1(3), 291-316. https://doi.org/10.1023/A:1009700419189
  5. Fawcett, T. and Provost, F. (1999), Activity monitoring : Noticing interesting changes in behavior, In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 53-62
  6. Guo, X., Yin, Y., Dong, C., Yang, G., and Zhou, G. (2008), On the class imbalance problem, In Natural Computation, ICNC, Fourth International Conference on IEEE, 4, 192-201.
  7. Gustafson, T. L. (2000), Practical risk-adjusted quality control charts for infection control, American journal of infection control, 28(6), 406-414. https://doi.org/10.1067/mic.2000.109883
  8. Jiang, W., Au, T., and Tsui, K. L. (2007), A statistical process control approach to business activity monitoring, IIE Transactions, 39(3), 235-249. https://doi.org/10.1080/07408170600743912
  9. Khan, S. S. and Madden, M. G. (2013), One-Class Classification : Taxonomy of Study and Review of Techniques, arXiv preprint arXiv : 1312.0049.
  10. Kumar, S. and Spafford, E. H. (1994), A pattern matching model for misuse intrusion detection, In Proceedings of the National Computer Security Conference, 11-21.
  11. Li, R., He, W., Wu, Z., Hu, J., and Liu, Y. (2012), Modeling user's temporal dynamic profile in micro-blogging using clustering method. In Information Science and Service Science and Data Mining (ISSDM), 2012 6th International Conference on New Trends in IEEE, 808-812.
  12. Lourenco, A. and Belo, O. (2011), Clickstream Data Warehousing for Web Crawlers Profiling, In Proceedings of the World Congress on Engineering.
  13. Lucas, J. M. and Saccucci, M. S. (1990), Exponentially weighted moving average control schemes : properties and enhancements, Technometrics, 32(1), 1-12. https://doi.org/10.1080/00401706.1990.10484583
  14. Montgomery, D. C. (2007), Introduction to statistical quality control. John Wiley and Sons.
  15. Phua, C., Lee, V., Smith, K., and Gayler, R. (2010), A comprehensive survey of data mining-based fraud detection research, arXiv preprint arXiv : 1009.6119.
  16. Puttagunta, V. and Kalpakis, K. (2002), Adaptive Methods for Activity Monitoring of Streaming Data, In ICMLA, 2, 197-203.
  17. Qian, Z., Jiang, W., and Tsui, K. L. (2006), Churn detection via customer profile modelling, International Journal of Production Research, 44(14), 2913-2933. https://doi.org/10.1080/00207540600632240
  18. Raghavan, N., Bell, R. M., and Schonlau, M. (2000), Defection detection, In Proc, 6, 447-456.
  19. Roth, V. (2004), Outlier detection with one-class kernel Fisher discriminants, In Advances in Neural Information Processing Systems, 1169-1176.
  20. Song, X., Wu, M., Jermaine, C., and Ranka, S. (2007), Conditional anomaly detection, Knowledge and Data Engineering, IEEE Transactions, 19(5), 631-645.
  21. Sukchotrat, T., Kim, S. B., and Tsung, F. (2009), One-class classification- based control charts for multivariate process monitoring, IIE Transactions, 42(2), 107-120. https://doi.org/10.1080/07408170903019150
  22. Tax, D. M. J. (2001), One-Class Classification : Concept-Learning in the Absence of Counter-Examples, Ph.D. thesis, Delf University of Technology, The Netherlands.