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When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.
Outliers in statistical data , volume 3
Barnett, V. and Lewis, T · 1994
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Measuring Mass Concentrations and Estimating Density Contour Cluster-An excess Mass Approach
Polonik, W · 1995
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Minimum volume sets and generalized quantile processes
Polonik, W · 1997
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The third international knowledge discovery and data mining tools competition dataset
KDDCup · 1999
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LOF: identifying density-based local outliers
Breunig, M.M., Kriegel, H.P., Ng, R.T., and Sander, J · 2000
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Anomaly detection over noisy data using learned probability distributions
Eskin, E · 2000
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On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Yamanishi, K., Takeuchi, J.I., Williams, G., and Milne, P · 2000
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Outlier detection for high dimensional data
Aggarwal, C.C. and Yu, P.S · 2001
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Estimating the support of a high-dimensional distribution
Schölkopf, B., Platt, J.C, Shawe-Taylor, J., Smola, A.J, and Williamson, R.C · 2001
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Novelty detection: a review part 1: statistical approaches
Markou, M. and Singh, S · 2003
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A novel anomaly detection scheme based on principal component classifier
Shyu, M.L., Chen, S.C., Sarinnapakorn, K., and Chang, L · 2003
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A survey of outlier detection methodologies
Hodge, V.J. and Austin, J · 2004
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Kernel methods for pattern analysis
Shawe-Taylor, J. and Cristianini, N · 2004
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Learning minimum volume sets
Scott, C.D and Nowak, R.D · 2006
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An overview of anomaly detection techniques: Existing solutions and latest technological trends
Patcha, A. and Park, J.M · 2007
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A detailed analysis of the kdd cup 99 data set
Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A · 2009
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al · 2011
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On Evaluation of Outlier Rankings and Outlier Scores
Schubert, E., Wojdanowski, R., Zimek, A., and Kriegel, H.-P · 2012
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Scoring anomalies: a M-estimation approach
Clémençon, S. and Jakubowicz, J · 2013
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UCI machine learning repository, 2013
Lichman, M · 2013
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Anomaly Ranking as Supervised Bipartite Ranking
Clémençon, S. and Robbiano, S · 2014
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Isolation Forest
Liu, F.T., Ting, K.M., and Zhou, Z.H · 2008
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Anomaly detection: A survey
Chandola, V., Banerjee, A., and Kumar, V · 2009
Cited alongside, same era.
On Anomaly Ranking and Excess-Mass Curves
Goix, N., Sabourin, A., and Clémençon, S · 2015
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Calibration of One-Class SVM for MV set estimation
Thomas, A., Feuillard, V., and Gramfort, A · 2015
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