2019 • In KAIAFAS, Georgios; HAMMERSCHMIDT, Christian; STATE, Radu (Eds.) 16th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2019)
C. C. Aggarwal and S. Sathe. Outlier ensembles: An introduction. Springer, 2017.
G. O. Campos, A. Zimek, J. Sander, R. J. Campello, B. Micenková, E. Schubert, I. Assent, and M. E. Houle. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30 (4): 891-927, 2016.
M. Dowle and A. Srinivasan. data. Table: Extension of 'data. frame', 2018. R package version 1. 11. 8.
M. Goldstein and S. Uchida. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11 (4): e0152173, 2016.
F. Keller, E. Muller, and K. Bohm. Hics: high contrast subspaces for density-based outlier ranking. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pages 1037-1048. IEEE, 2012.
H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek. Loop: local outlier probabilities. In Proceedings of the 18th ACM conference on Information and knowledge management, pages 1649-1652. ACM, 2009.
A. Lazarevic and V. Kumar. Feature bagging for outlier detection. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 157-166. ACM, 2005.
F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining, pages 413-422. IEEE, 2008.
J. Madsen. Ddoutlier v0. 1. 0.
B. Micenková, B. McWilliams, and I. Assent. Learning representations for outlier detection on a budget. arXiv preprint arXiv: 1507. 08104, 2015.
E. Muller, I. Assent, P. Iglesias, Y. Mulle, and K. Bohm. Outlier ranking via subspace analysis in multiple views of the data. In Data Mining (ICDM), 2012 IEEE 12th International Conference on, pages 529-538. IEEE, 2012.
J. R. Pasillas-Díaz and S. Ratté. Bagged subspaces for unsupervised outlier detection. Computational Intelligence, 33 (3): 507-523, 2017.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in python. Journal of machine learning research, 12 (Oct): 2825-2830, 2011.
S. Rayana. Odds library. Stony Brook,-2016. NY: Stony Brook University, Department of Computer Science. URL: http://odds. cs. stonybrook. edu (, 2017), 2016.
S. Sathe and C. C. Aggarwal. Subspace histograms for outlier detection in linear time. Knowledge and Information Systems, pages 1-25, 2018.
E. Schubert, R. Wojdanowski, A. Zimek, and H.-P. Kriegel. On evaluation of outlier rankings and outlier scores. In Proceedings of the 2012 SIAM International Conference on Data Mining, pages 1047-1058. SIAM, 2012.
E. Schubert, A. Zimek, and H.-P. Kriegel. Generalized outlier detection with flexible kernel density estimates. In Proceedings of the 2014 SIAM International Conference on Data Mining, pages 542-550. SIAM, 2014.
B. van Stein, M. van Leeuwen, and T. Bäck. Local subspace-based outlier detection using global neighbourhoods. In Big Data (Big Data), 2016 IEEE International Conference on, pages 1136-1142. IEEE, 2016.
Y. Zhao and M. K. Hryniewicki. Xgbod: improving supervised outlier detection with unsupervised representation learning. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2018.
A. Zimek, R. J. Campello, and J. Sander. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. Acm Sigkdd Explorations Newsletter, 15 (1): 11-22, 2014.
A. Zimek, M. Gaudet, R. J. Campello, and J. Sander. Subsampling for efficient and effective unsupervised outlier detection ensembles. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 428-436. ACM, 2013.
A. Zimek, E. Schubert, and H.-P. Kriegel. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 5 (5): 363-387, 2012.
