Alguliyev, R.M., Aliguliyev, R.M., Imamverdiyev, Y.N. and Sukhostat, L.V. (2017). "An anomaly detection based on optimization", International Journal of Intelligent Systems and Applications, 9(12), 87-96.
Alguliyev, R., Aliguliyev, R. and Sukhostat, L. (2017). "Anomaly detection in Big data based on clustering", Statistics, Optimization and Information Computing, 5(4), 325-340.
Beliakov,
G., Kelarev, A. and Yearwood, J. (2011). "Robust Artificial Neural Networks and Outlier Detection",
Journal of Mathematical Programming and Operations Research, 61(12), 1467-1490 , Deakin University, Australia.
Benjamini, Y. (1988). "Opening the Box of a Boxplot", The American Statistician, 42(4), 257-262.
Bai, M.,
Wang, X.,
Xin, J. and
Wang, G. (2016). "An efficient algorithm for distributed density-based outlier detection on big data",
Neurocomputing, 181(C), 139-146.
Bai, L.,
Liang, J. and
Dang, C. (2011). "An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data",
Knowledge-Based Systems, 24(6), 785-795.
Ester, M., Kriegel, H-P., Sander, J. and Xu, X. (1996). "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", KDD'96 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining,Institute for Computers Science, University of Munich, Germany, 226-231.
Frigge, M., Hoaglin, D.C. and Iglewicz, B. (1989). "Some Implementations of the Boxplot", The American Statistician, 43(1), 50-54.
Gaffney, J. and Ulvila, J. (2001). "Evaluation of intrusion detectors: A decision theory approach", In Proceedings of IEEE Symposium on Security and Privacy, Oakland, CA, USA, 50-61.
Hand, D., Mannila, H. and Smyth, P. (2001). "rinciples of data mining, The MIT Press.
Johnson, R. and Wichern,
D. (1992)."
Applied multivariate statistical analysis, Prentice Hall.
Jiang, F., Liu, G.,
Du, J. and
Sui, Y. (2016). "Initialization of
K-modes clustering using outlier detection techniques”,
Information Sciences, 332, 167-183.
Karim, A.N.M., Nordin, A.N. and Begum, S. (2014), "Technical and Economic Feasibility of Sensor Technology for Health/Environmental Condition Monitoring", Comprehensive Materials Processing, 13, 499-514.
Latecki, L. J., Lazarevic, A. and Pokrajac, D. (2007). "Outlier Detection with Kernel Density Functions", 5th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM), Leipzig, Germany, pp. 61-75.
Loureiro, A., Torgo,
L. and Soares, C. (2004). "Outlier detection using clustering methods: A data cleaning application",
In proceedings of the data mining for business workshop, University of Porto, Porto, Portugal.
Massart, D.L., Smeyers-Verbeke, A., Capron, X. and Schlesier, K. (2005). "Practical data handling visual presentation of data by means of box plots", Journal of Vrije Universiteit Brussel, 18(4), 215-218.
Montgomery, D.C., Peck, E.A. and Vining, G.G. (2012). Introduction to Linear Regression Analysis, 3rd Edition, John Wiley & Sons, New York, USA.
Motulsky, H. and Brown, R. (2006). "Detecting outliers when fitting data with nonlinear regression: A new method based on robust nonlinear regression and the false discovery rate", BMC Bioinformatics, 7(123), 1471-2105.
Ni, Y.Q. (2014). "Structural health monitoring of cable-supported bridges based on vibration measurements", Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014, Porto, Portugal, pp. 65-72.
Sinwar, D. and Kaushik, R. (2014). "Study of Euclidean and Manhattan Distance Metrics using simple K-means clustering", International Journal for Research in Applied Science and Engineering Technology, 2, 270-274.
Zhuang, W., Zhang, Y. and Grassle, J.F. (2004). "Identifying erroneous data using outlier detection techniques", Proceedings Ocean Biodiversity Informatics, International Conference on Marine Biodiversity Data Management, Hamburg, Germany, 37, 187-192.
)
