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An outlier is an observation or a data point that is far from rest of the data points in a given dataset or we can be said that an outlier is away from the center of mass of observations.
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Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: SIGMOD (2000)
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Tang, J., Chen, Z., Fu, A.W.C., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: PAKDD (2002)
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Papadimitriou, S., Kitagawa, H., Gibbons, P.B., Faloutsos, C.: Loci: Fast outlier detection using the local correlation integral. In: ICDE (2003)
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Hodge, V., Austin, J.: A survey of outlier detection methodologies. AI Review (2004)
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Acuña, E., Rodríguez, C.: An empirical study of the effect of outliers on the misclassification error rate (2005)
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Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: KDD (2005)
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Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T.: Inlier-based outlier detection via direct density ratio estimation. In: ICDM (2008)
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Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data (2008)
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Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: ICDM (2008)
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Yang, P., Huang, B.: Knn based outlier detection algorithm in large dataset. In: ETTANDGRS (2008)
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Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: Techniques, systems and challenges. computers & security (2009)
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Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: Loop: local outlier probabilities. In: CIKM (2009)
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Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: PAKDD (2009)
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Cousineau, D., Chartier, S.: Outliers detection and treatments. IJPR (2010)
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Hubert, M., Debruyne, M.: Minimum covariance determinant. WIREs: CS (2010)
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Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: ICDM (2017)
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Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain mr images. In: MICCAI Brainlesion Workshop (2018)
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Leys, C., Klein, O., Dominicy, Y., Ley, C.: Detecting multivariate outliers: Use a robust variant of the mahalanobis distance. JESP (2018)
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Pham, N.: L1-depth revisited: A robust angle-based outlier factor in high-dimensional space. In: ECML PKDD (2018)
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Zhao, Y., Hryniewicki, M.K.: Xgbod: improving supervised outlier detection with unsupervised representation learning. In: IJCNN (2018)
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Goldstein, M., Dengel, A.: Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm (2012)
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Leys, C., Ley, C., Klein, O., Bernard, P., Licata, L.: Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. JESPs (2013)
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Bounsiar, A., Madden, M.G.: One-class support vector machines revisited. In: ICISA (2014)
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Hand, D.J., Adams, N.M.: Data mining. Wiley StatsRef (2014)
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Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. FGCS (2016)
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Liang, J., Parthasarathy, S.: Robust contextual outlier detection: Where context meets sparsity. In: CIKM (2016)
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Pevný, T.: Loda: Lightweight on-line detector of anomalies. ML (2016)
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Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M., He, X.: Generative adversarial active learning for unsupervised outlier detection. TKDE 32
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Mowbray, F.I., Fox-Wasylyshyn, S.M., El-Masri, M.M.: Univariate outliers: A conceptual overview for the nurse researcher. CJNR (2019)
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Zhao, Y., Nasrullah, Z., Hryniewicki, M.K., Li, Z.: Lscp: Locally selective combination in parallel outlier ensembles. In: ICDM (2019)
2019
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Zhao, Y., Nasrullah, Z., Li, Z.: Pyod: A python toolbox for scalable outlier detection. JMLR (2019)
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Li, Z., Zhao, Y., Botta, N., Ionescu, C., Hu, X.: Copod: copula-based outlier detection. arXiv (2020)
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Li, Z., Zhao, Y., Fu, J.: Sync: A copula based framework for generating synthetic data from aggregated sources. arXiv (2020)
2020
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