References

  1. U.S. EPA, Water Security Initiative: System Evaluation of the Cincinnati Contamination Warning system Pilot, U.S. EPA Water Security Division, Washington, D.C., 2014.
  2. U.S. EPA, Water Security Initiative: Evaluation of the Water Quality Monitoring Component of the Cincinnati Contamination Warning System Pilot, U.S. EPA Water Security Division, Washington, D.C., 2014.
  3. U.S. EPA, Water Quality Event Detection Systems for Drinking Water Contamination Warning System (EPA/600/R-010/036), Office of Research and Development, National Homeland Security Research Center, Washington, D.C., 2010.
  4. R.B. Robinson, C.D. Cox, K. Odom, Identifying outliers in correlated water quality data, J. Environ. Eng., 131 (2005) 651–657.
  5. D. Hawkins, Identification of Outlier, Chapman and Hall, London, 1980.
  6. J.W. Osborne, A. Overbay, The power of outliers (and why researchers should always check for them), Pract. Assess. Res. Eval., 9 (2004) 1–12.
  7. H.P. Kriegel, P. Kroger, A. Zimk, Outlier Detection Techniques, Tutorial at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 10, 2009.
  8. O. Malmon, L. Rockach, Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers, Boston, MA, 2005.
  9. A. Arning, R. Agrawal, P. Raghavan, A Linear Method for Deviation Detection in Large Databases, In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, AAAI Press, Portland, Oregon, 1996, pp. 164–169.
  10. M.M. Breunig, H.P. Kriegel, R.T. Ng, J. Sander, LOF: Identifying Density-Based Local Outliers, In ACM SIGMOD Record, 29 (2000) 93–104.
  11. N.A. Yousri, M.A. Lsmail, M.S. Kamel, Fuzzy Outlier Analysis: A Combined Clustering – Outlier Detection Approach, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2007, pp. 412–418.
  12. M.B. Al-Zoubi, A.D. Ali, A.A. Yahya, Fuzzy Clustering- Based Approach for Outlier Detection, Proceedings of the 9th WSEAS International Conference on Applications of Computer Engineering, 2008, pp. 192–197.
  13. R. Ostermark, A fuzzy vector valued kNN-algorithm for automatic outlier detection, Appl. Soft Comput., 9 (2009) 1263–1272.
  14. V. Barnett, T. Lewis, Outliers in Statistical Data, 3rd ed., John Wiley & Sons, New Jersey, 1994.
  15. U.S. EPA, Data Quality Assessment: Statistical Methods for Practitioners, EPA QA/G-9S, 2006.
  16. K.P. Murphy, A Probabilistic Perspective, The MIT Press, London, 2012.
  17. P. Filzmoser, R.G. Garrett, C. Reimann, Multivariate outlier detection in exploration geochemistry, Comput. Geosci., 31 (2005) 579–587.
  18. J.D. Jobson, Applied Multivariate Data Analysis, Springer-Verlag, New York, 1992.
  19. A.C. Rencher, Multivariate Statistical Inference, Wiley, New York, 1998.
  20. P.J. Rousseeuw, B.C. van Zomeren, Unmasking multivariate outliers and leverage points, J. Am. Stat. Assoc., 85 (1990) 633–639.
  21. D.R. Anderson, D.J. Sweeney, T.W. Williams, Statistics for Business and Economics, West, Minneapolis/St. Paul, 1993.