| [1] |
Markou, M., & Singh, S. (2003). Novelty detection: a review—part 1: statistical approaches. Signal processing, 83(12), 2481-2497. |
| [2] |
Markou, M., & Singh, S. (2003). Novelty detection: a review—part 2:: neural network based approaches. Signal processing, 83(12), 2499-2521. |
| [3] |
Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial intelligence review, 22(2), 85-126. |
| [4] |
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. |
| [5] |
Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422). IEEE. |
| [6] |
Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 93-104). |
| [7] |
Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2003, March). Loci: Fast outlier detection using the local correlation integral. In Proceedings 19th international conference on data engineering (Cat. No. 03CH37405) (pp. 315-326). IEEE. |
| [8] |
Aryal, S., Ting, K. M., & Haffari, G. (2016, April). Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection. In Pacific-Asia Workshop on Intelligence and Security Informatics (pp. 73-86). Springer, Cham. |
| [9] |
Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (1999, September). Optics-of: Identifying local outliers. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 262-270). Springer, Berlin, Heidelberg. |
| [10] |
Liu, F. T., Ting, K. M., & Zhou, Z. H. (2012). Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1), 1-39. |
| [11] |
Aryal, S., Ting, K. M., Wells, J. R., & Washio, T. (2014, May). Improving iforest with relative mass. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 510-521). Springer, Cham. |
| [12] |
Liu, F. T., Ting, K. M., & Zhou, Z. H. (2010, September). On detecting clustered anomalies using SCiForest. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 274-290). Springer, Berlin, Heidelberg. |
| [13] |
Pang, G., Ting, K. M., & Albrecht, D. (2015, November). LeSiNN: Detecting anomalies by identifying least similar nearest neighbours. In 2015 IEEE international conference on data mining workshop (ICDMW) (pp. 623-630). IEEE. |
| [14] |
Agrawal, S., & Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, 708-713. |
| [15] |
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119. |
| [16] |
Zimek, A., Schubert, E., & Kriegel, H. P. (2012). 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. |
| [17] |
Ramaswamy, S., Rastogi, R., & Shim, K. (2000, May). Efficient algorithms for mining outliers from large data sets. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 427-438). |
| [18] |
Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016). Discovering outlying aspects in large datasets. Data mining and knowledge discovery, 30(6), 1520-1555. |
| [19] |
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015). Mining outlying aspects on numeric data. Data Mining and Knowledge Discovery, 29(5), 1116-1151. |
| [20] |
Zhang, J., Lou, M., Ling, T. W., & Wang, H. (2004). HOS-miner: A system for detecting outlying subspaces of high-dimensional data. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB'04) (pp. 1265-1268). Morgan Kaufmann Publishers Inc.. |
| [21] |
Keller, F., Muller, E., & Bohm, K. (2012, April). HiCS: High contrast subspaces for density-based outlier ranking. In 2012 IEEE 28th international conference on data engineering (pp. 1037-1048). IEEE. |
| [22] |
Steinbuss, G., & Böhm, K. (2017). Hiding outliers in high-dimensional data spaces. International Journal of Data Science and Analytics, 4(3), 173-189. |
| [23] |
Pang, G., Ting, K. M., Albrecht, D., & Jin, H. (2016). ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets. Journal of Artificial Intelligence Research, 57, 593-620. |
| [24] |
Agrawal, A. (2009, August). Local subspace based outlier detection. In International Conference on Contemporary Computing (pp. 149-157). Springer, Berlin, Heidelberg. |
| [25] |
Sathe, S., & Aggarwal, C. C. (2016, December). Subspace outlier detection in linear time with randomized hashing. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 459-468). IEEE. |
| [26] |
Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2012, December). Outlier detection in arbitrarily oriented subspaces. In 2012 IEEE 12th international conference on data mining (pp. 379-388). IEEE. |
| [27] |
Zhang, L., Lin, J., & Karim, R. (2015). An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection. Reliability Engineering & System Safety, 142, 482-497. |
| [28] |
Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009, April). Outlier detection in axis-parallel subspaces of high dimensional data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 831-838). Springer, Berlin, Heidelberg. |
| [29] |
Vinh, N. X., Chan, J., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2015, May). Scalable outlying-inlying aspects discovery via feature ranking. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 422-434). Springer, Cham. |
| [30] |
Zhang, K., Hutter, M., & Jin, H. (2009, April). A new local distance-based outlier detection approach for scattered real-world data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 813-822). Springer, Berlin, Heidelberg. |
| [31] |
Tran, L., Fan, L., & Shahabi, C. (2016). Distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 9(12), 1089-1100. |
| [32] |
Sugiyama, M., & Borgwardt, K. (2013). Rapid distance-based outlier detection via sampling. In Advances in Neural Information Processing Systems (pp. 467-475). |
| [33] |
Angiulli, F., & Pizzuti, C. (2002, August). Fast outlier detection in high dimensional spaces. In European conference on principles of data mining and knowledge discovery (pp. 15-27). Springer, Berlin, Heidelberg. |
| [34] |
Duan, L., Xu, L., Liu, Y., & Lee, J. (2009). Cluster-based outlier detection. Annals of Operations Research, 168(1), 151-168. |
| [35] |
Jiang, S. Y., & An, Q. B. (2008, October). Clustering-based outlier detection method. In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 2, pp. 429-433). IEEE. |
| [36] |
Elahi, M., Li, K., Nisar, W., Lv, X., & Wang, H. (2008, October). Efficient clustering-based outlier detection algorithm for dynamic data stream. In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 5, pp. 298-304). IEEE. |
| [37] |
Pamula, R., Deka, J. K., & Nandi, S. (2011, February). An outlier detection method based on clustering. In 2011 Second International Conference on Emerging Applications of Information Technology (pp. 253-256). IEEE. |
| [38] |
Christy, A., Gandhi, G. M., & Vaithyasubramanian, S. (2015). Cluster based outlier detection algorithm for healthcare data. Procedia Computer Science, 50, 209-215. |
| [39] |
Wang, X., Wang, X. L., & Wilkes, D. M. (2012, July). A minimum spanning tree-inspired clustering-based outlier detection technique. In Industrial Conference on Data Mining (pp. 209-223). Springer, Berlin, Heidelberg. |
| [40] |
Jiang, S. Y., & Yang, A. M. (2009, August). Framework of clustering-based outlier detection. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 1, pp. 475-479). IEEE. |
| [41] |
Samariya, D., Aryal, S., Ting, K. M., & Ma, J. (2020, October). A new effective and efficient measure for outlying aspect mining. In International Conference on Web Information Systems Engineering (pp. 463-474). Springer, Cham. |
| [42] |
T. R. Bandaragoda, K. M. Ting, D. Albrecht, F. T. Liu and J. R. Wells, "Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble," 2014 IEEE International Conference on Data Mining Workshop, Shenzhen, 2014, pp. 698-705, doi: 10.1109/ICDMW.2014.70. |
| [43] |
Ting, K. M., Aryal, S., & Washio, T. (2018, November). Which Outlier Detector Should I use?. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 8-8). IEEE. |
| [44] |
Kriegel, H. P., Kröger, P., & Zimek, A. (2010). Outlier detection techniques. Tutorial at KDD, 10, 1-76. |
| [45] |
Bandaragoda, T. R., Ting, K. M., Albrecht, D., Liu, F. T., Zhu, Y., & Wells, J. R. (2018). Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence, 34(4), 968-998. |
| [46] |
Wang, H., Bah, M. J., & Hammad, M. (2019). Progress in outlier detection techniques: A survey. IEEE Access, 7, 107964-108000. |
| [47] |
Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: a survey. Data mining and knowledge discovery, 29(3), 626-688. |
| [48] |
Domingues, R., Filippone, M., Michiardi, P., & Zouaoui, J. (2018). A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, 406-421. |
| [49] |
Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. |
| [50] |
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2020). Deep learning for anomaly detection: A review. arXiv preprint arXiv:2007.02500. |
| [51] |
Aryal, S., Baniya, A. A., & Santosh, K. C. (2019). Improved histogram-based anomaly detector with the extended principal component features. arXiv preprint arXiv:1909.12702. |
| [52] |
Wells, J. R., Ting, K. M., & Washio, T. (2014). LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognition, 47(8), 2702-2720. |
| [53] |
Aryal, S. (2018, June). Anomaly detection technique robust to units and scales of measurement. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 589-601). Springer, Cham. |
| [54] |
Aryal, S., Santosh, K. C., & Dazeley, R. (2020). usfAD: a robust anomaly detector based on unsupervised stochastic forest. International Journal of Machine Learning and Cybernetics, 1-14. |
| [55] |
Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002, May). Enhancing effectiveness of outlier detections for low density patterns. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 535-548). Springer, Berlin, Heidelberg. |
| [56] |
Schubert, E., Zimek, A., & Kriegel, H. P. (2014). Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data mining and knowledge discovery, 28(1), 190-237. |
| [57] |
Ren, D., Wang, B., & Perrizo, W. (2004, November). Rdf: A density-based outlier detection method using vertical data representation. In Fourth IEEE International Conference on Data Mining (ICDM'04) (pp. 503-506). IEEE. |
| [58] |
Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009, November). LoOP: local outlier probabilities. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 1649-1652). |
| [59] |
Fan, H., Zaïane, O. R., Foss, A., & Wu, J. (2009). Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data. Knowledge and Information Systems, 19(1), 31-51. |
| [60] |
Goldstein, M. (2012, November). FastLOF: An expectation-maximization based local outlier detection algorithm. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 2282-2285). IEEE. |
| [61] |
Campello, R. J., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(1), 1-51. |
| [62] |
Pevný, T. (2016). Loda: Lightweight on-line detector of anomalies. Machine Learning, 102(2), 275-304. |
| [63] |
Zhao, Y., Nasrullah, Z., Hryniewicki, M. K., & Li, Z. (2019, May). LSCP: Locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 585-593). Society for Industrial and Applied Mathematics. |
| [64] |
Zhao, Y., & Hryniewicki, M. K. (2019). DCSO: dynamic combination of detector scores for outlier ensembles. arXiv preprint arXiv:1911.10418. |
| [65] |
Du, M., Li, F., Zheng, G., & Srikumar, V. (2017, October). Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1285-1298). |
| [66] |
Zhao, L., & Akoglu, L. (2020). On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights. arXiv preprint arXiv:2012.12931. |
| [67] |
Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: a survey. Data mining and knowledge discovery, 29(3), 626-688. |
