1. Cansado, A., Soto, A., 2008. Unsupervised anomaly detection in large databases using Bayesian networks. Appl. Artif. Intell., 22(4):309–330. [doi:10.1080/08839510801972801]
2. Eskin, E., 2000. Anomaly Detection over Noisy Data Using Learned Probability Distributions. Proc. Int. Conf. on Machine Learning, p.255–262. [doi:10.1109/ICCSA.2008.70]
3. Eskin, E., Arnold, A., Prerau, M., Portony, L., Stolfo, S., 2002. A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In: Barbara, E., Jajodia, S. (Eds.), Applications of Data Mining in Computer Security. Kluwer Academic Publishers, Norwell, MA, USA, p.272.
4. Ismail, A.S.H., Abdullah, A.H., Bak, K.B.A., Nqudi, M.A., Dahlan, D., Chimphlee, W., 2008. A Novel Method for Unsupervised Anomaly Detection Using Unlabelled Data. Proc. Int. Conf. on Computational Sciences and Its Applications., p.252–260. [doi:10.1109/ICCSA.2008.70]
5. Knorr, E.M., 2002. Outliers and Data Mining: Finding Exceptions in Data. PhD Thesis, University of British Columbia, Canada, p.74.