{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:16:14Z","timestamp":1781518574625,"version":"3.54.1"},"reference-count":107,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["The VLDB Journal"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00778-021-00721-1","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:03:51Z","timestamp":1643155431000},"page":"977-1008","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["A survey on outlier explanations"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6681-7847","authenticated-orcid":false,"given":"Egawati","family":"Panjei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-4747","authenticated-orcid":false,"given":"Le","family":"Gruenwald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3055-1845","authenticated-orcid":false,"given":"Eleazar","family":"Leal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shejuti","family":"Silvia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,26]]},"reference":[{"key":"721_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.04.007","author":"A Abdallah","year":"2016","unstructured":"Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. (2016). https:\/\/doi.org\/10.1016\/j.jnca.2016.04.007","journal-title":"J. Netw. Comput. Appl."},{"issue":"2","key":"721_CR2","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1111\/fima.12269","volume":"48","author":"J Adams","year":"2019","unstructured":"Adams, J., Hayunga, D., Mansi, S., Reeb, D., Verardi, V.: Identifying and treating outliers in finance. Financ. Manag. 48(2), 345\u2013384 (2019). https:\/\/doi.org\/10.1111\/fima.12269","journal-title":"Financ. Manag."},{"key":"721_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3","volume-title":"Outlier Analysis","author":"C Aggarwal","year":"2017","unstructured":"Aggarwal, C.: Outlier Analysis. Springer, New York (2017)"},{"key":"721_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-47578-3_1","volume-title":"An Introduction to Outlier Analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal, C.C.: An Introduction to Outlier Analysis, pp. 1\u201334. Springer, Berlin (2017). https:\/\/doi.org\/10.1007\/978-3-319-47578-3_1"},{"issue":"2","key":"721_CR5","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1145\/170036.170072","volume":"22","author":"R Agrawal","year":"1993","unstructured":"Agrawal, R., Imieli\u0144ski, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207\u2013216 (1993). https:\/\/doi.org\/10.1145\/170036.170072","journal-title":"SIGMOD Rec."},{"key":"721_CR6","unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487\u2013499. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1994)"},{"key":"721_CR7","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.future.2015.01.001","volume":"55","author":"M Ahmed","year":"2016","unstructured":"Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278\u2013288 (2016). https:\/\/doi.org\/10.1016\/j.future.2015.01.001","journal-title":"Future Gener. Comput. Syst."},{"key":"721_CR8","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2014","unstructured":"Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Discov. 29, 626\u2013688 (2014)","journal-title":"Data Min. Knowl. Discov."},{"issue":"5","key":"721_CR9","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TDSC.2018.2821693","volume":"17","author":"J Alvarez Cid-Fuentes","year":"2020","unstructured":"Alvarez Cid-Fuentes, J., Szabo, C., Falkner, K.: Adaptive performance anomaly detection in distributed systems using online SVMs. IEEE Trans. Dependable Secure Comput. 17(5), 928\u2013941 (2020). https:\/\/doi.org\/10.1109\/TDSC.2018.2821693","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"721_CR10","doi-asserted-by":"publisher","unstructured":"Amarasinghe, K., Kenney, K., Manic, M.: Toward explainable deep neural network based anomaly detection. In: 2018 11th International Conference on Human System Interaction (HSI), pp. 311\u2013317 (2018). https:\/\/doi.org\/10.1109\/HSI.2018.8430788","DOI":"10.1109\/HSI.2018.8430788"},{"key":"721_CR11","doi-asserted-by":"crossref","unstructured":"Amato, A.: On the role of distributed computing in big data analytics. In: Distributed Computing in Big Data Analytics, Scalable Computing and Communications, pp. 1\u201310. Springer International Publishing (2017)","DOI":"10.1007\/978-3-319-59834-5_1"},{"key":"721_CR12","doi-asserted-by":"publisher","unstructured":"Avritzer, A., Tanikella, R., James, K., Cole, R.G., Weyuker, E.: Monitoring for security intrusion using performance signatures. In: WOSP\/SIPEW\u201910 - Proceedings of the 1st Joint WOSP\/SIPEW International Conference on Performance Engineering (2010). https:\/\/doi.org\/10.1145\/1712605.1712623","DOI":"10.1145\/1712605.1712623"},{"key":"721_CR13","doi-asserted-by":"publisher","unstructured":"Bialas, A., Michalak, M., Flisiuk, B.: Anomaly Detection in network traffic security assurance. In: Advances in Intelligent Systems and Computing, vol. 987 (2020). https:\/\/doi.org\/10.1007\/978-3-030-19501-4_5","DOI":"10.1007\/978-3-030-19501-4_5"},{"key":"721_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.112896","author":"C Bigoni","year":"2020","unstructured":"Bigoni, C., Hesthaven, J.S.: Simulation-based anomaly detection and damage localization: an application to structural health monitoring. Comput. Methods Appl. Mech. Eng. (2020). https:\/\/doi.org\/10.1016\/j.cma.2020.112896","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"10","key":"721_CR15","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.14778\/3467861.3467863","volume":"14","author":"P Bonial","year":"2021","unstructured":"Bonial, P., Paparrizos, J., Palpanas, T., Franklin, M.J.: SAND: streaming subsequence anomaly detection. Proc. VLDB Endow. 14(10), 1717\u20131729 (2021). https:\/\/doi.org\/10.14778\/3467861.3467863","journal-title":"Proc. VLDB Endow."},{"key":"721_CR16","unstructured":"Borowski, S.: The origin and popular use of Occam\u2019s razor (2012). https:\/\/www.aaas.org\/origin-and-popular-use-occams-razor"},{"issue":"3","key":"721_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381028","volume":"53","author":"A Boukerche","year":"2020","unstructured":"Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. 53(3), 1\u201337 (2020). https:\/\/doi.org\/10.1145\/3381028","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"721_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"MM Breunig","year":"2000","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93\u2013104 (2000). https:\/\/doi.org\/10.1145\/335191.335388","journal-title":"SIGMOD Rec."},{"key":"721_CR19","doi-asserted-by":"crossref","unstructured":"Buchbinder, N., Feldman, M., Naor, J.S., Schwartz, R.: Submodular maximization with cardinality constraints. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1433\u20131452 (2014)","DOI":"10.1137\/1.9781611973730.80"},{"key":"721_CR20","doi-asserted-by":"publisher","first-page":"15:1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 15:1-15:58 (2009)","journal-title":"ACM Comput. Surv."},{"key":"721_CR21","unstructured":"Chen, Z., Tang, J., Fu, A.W.C.: Modeling and efficient mining of intentional knowledge of outliers. In: Seventh International Database Engineering and Applications Symposium, pp. 44\u201353 (2003)"},{"issue":"7","key":"721_CR22","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","volume":"7","author":"AA Cook","year":"2020","unstructured":"Cook, A.A., Misirli, G., Fan, Z.: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481\u20136494 (2020). https:\/\/doi.org\/10.1109\/JIOT.2019.2958185","journal-title":"IEEE Internet Things J."},{"key":"721_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"2004","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (2004)","journal-title":"Mach. Learn."},{"key":"721_CR24","doi-asserted-by":"crossref","unstructured":"Dang, X.H., Assent, I., Ng, R.T., Zimek, A., Schubert, E.: Discriminative features for identifying and interpreting outliers. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 88\u201399. IEEE (2014)","DOI":"10.1109\/ICDE.2014.6816642"},{"key":"721_CR25","doi-asserted-by":"crossref","unstructured":"Dang, X.H., Micenkov\u00e1, B., Assent, I., Ng, R.T.: Local outlier detection with interpretation. In: Machine Learning and Knowledge Discovery in Databases, pp. 304\u2013320 (2013)","DOI":"10.1007\/978-3-642-40994-3_20"},{"key":"721_CR26","unstructured":"Defense Advanced Research Projects Agency. https:\/\/www.darpa.mil\/program\/transparent-computing. Accessed 4 Oct 2021"},{"key":"721_CR27","unstructured":"Deng, H.: Guided random forest in the RRF package. arXiv:1306.0237 (2013)"},{"issue":"20","key":"721_CR28","doi-asserted-by":"publisher","first-page":"5181","DOI":"10.1016\/j.jsv.2014.05.012","volume":"333","author":"N Dervilis","year":"2014","unstructured":"Dervilis, N., Cross, E., Barthorpe, R., Worden, K.: Robust methods of inclusive outlier analysis for structural health monitoring. J. Sound Vib. 333(20), 5181\u20135195 (2014). https:\/\/doi.org\/10.1016\/j.jsv.2014.05.012","journal-title":"J. Sound Vib."},{"key":"721_CR29","unstructured":"Desai, S.: Research guides: fake news, lies and propaganda: how to sort fact from fiction: what is fake news (2018)"},{"key":"721_CR30","doi-asserted-by":"crossref","unstructured":"Dutta, K.: Distributed computing technologies in big data analytics. In: Distributed Computing in Big Data Analytics, Scalable Computing and Communications, pp. 57\u201382. Springer (2017)","DOI":"10.1007\/978-3-319-59834-5_4"},{"key":"721_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/9781118443118","volume-title":"Structural Health Monitoring: A Machine Learning Perspective","author":"CR Farrar","year":"2012","unstructured":"Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, New York (2012). https:\/\/doi.org\/10.1002\/9781118443118"},{"key":"721_CR32","doi-asserted-by":"publisher","unstructured":"Feng, Q., Dou, Z., Li, C., Si, G.: Anomaly detection of spectrum in wireless communication via deep autoencoder. In: Lecture Notes in Electrical Engineering, vol. 421 (2017). https:\/\/doi.org\/10.1007\/978-981-10-3023-9_42","DOI":"10.1007\/978-981-10-3023-9_42"},{"issue":"7","key":"721_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3464423","volume":"54","author":"T Fernando","year":"2021","unstructured":"Fernando, T., Gammulle, H., Denman, S., Sridharan, S., Fookes, C.: Deep learning for medical anomaly detection\u2014a survey. ACM Comput. Surv. 54(7), 1\u201337 (2021). https:\/\/doi.org\/10.1145\/3464423","journal-title":"ACM Comput. Surv."},{"key":"721_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-020-01834-3","author":"N Fleming","year":"2020","unstructured":"Fleming, N.: Coronavirus misinformation, and how scientists can help to fight it. Nature (2020). https:\/\/doi.org\/10.1038\/d41586-020-01834-3","journal-title":"Nature"},{"key":"721_CR35","doi-asserted-by":"publisher","unstructured":"Fu, Q., Lou, J.G., Wang, Y., Li, J.: Execution anomaly detection in distributed systems through unstructured log analysis. In: Proceedings\u2014IEEE International Conference on Data Mining, ICDM (2009). https:\/\/doi.org\/10.1109\/ICDM.2009.60","DOI":"10.1109\/ICDM.2009.60"},{"issue":"11","key":"721_CR36","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/1839676.1839694","volume":"53","author":"M Garland","year":"2010","unstructured":"Garland, M., Kirk, D.B.: Understanding throughput-oriented architectures. Commun. ACM 53(11), 58\u201366 (2010). https:\/\/doi.org\/10.1145\/1839676.1839694","journal-title":"Commun. ACM"},{"key":"721_CR37","doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., Matwin, S., Pedreschi, D.: Black box explanation by learning image exemplars in the latent feature space. In: Machine Learning and Knowledge Discovery in Databases, pp. 189\u2013205 (2020)","DOI":"10.1007\/978-3-030-46150-8_12"},{"issue":"5","key":"721_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1\u201342 (2018). https:\/\/doi.org\/10.1145\/3236009","journal-title":"ACM Comput. Surv."},{"key":"721_CR39","doi-asserted-by":"crossref","unstructured":"Gupta, N., Eswaran, D., Shah, N., Akoglu, L., Faloutsos, C.: Beyond outlier detection: lookout for pictorial explanation. In: Machine Learning and Knowledge Discovery in Databases, pp. 122\u2013138. Springer (2019)","DOI":"10.1007\/978-3-030-10925-7_8"},{"key":"721_CR40","doi-asserted-by":"crossref","unstructured":"Hawkins, D.: Identification of outliers. In: Monographs on Applied Probability and Statistics (1980)","DOI":"10.1007\/978-94-015-3994-4"},{"key":"721_CR41","doi-asserted-by":"publisher","unstructured":"Herath, J.D., Yang, P., Yan, G.: Real-time evasion attacks against deep learning-based anomaly detection from distributed system logs. In: CODASPY 2021\u2014Proceedings of the 11th ACM Conference on Data and Application Security and Privacy (2021). https:\/\/doi.org\/10.1145\/3422337.3447833","DOI":"10.1145\/3422337.3447833"},{"key":"721_CR42","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.envsoft.2009.08.010","volume":"25","author":"DJ Hill","year":"2010","unstructured":"Hill, D.J., Minsker, B.S.: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach. Environ. Model. Softw. 25, 1014\u20131022 (2010)","journal-title":"Environ. Model. Softw."},{"key":"721_CR43","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1023\/B:AIRE.0000045502.10941.a9","volume":"22","author":"VJ Hodge","year":"2004","unstructured":"Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85\u2013126 (2004)","journal-title":"Artif. Intell. Rev."},{"issue":"8","key":"721_CR44","doi-asserted-by":"publisher","first-page":"10147","DOI":"10.1016\/j.eswa.2011.02.077","volume":"38","author":"B Huang","year":"2011","unstructured":"Huang, B., Yang, P.: Finding key knowledge attribute subspace of outliers in high-dimensional dataset. Expert Syst. Appl. 38(8), 10147\u201310152 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.02.077","journal-title":"Expert Syst. Appl."},{"key":"721_CR45","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264\u2013323 (1999)","journal-title":"ACM Comput. Surv."},{"key":"721_CR46","doi-asserted-by":"publisher","unstructured":"Jiang, J., Chen, J., Gu, T., Choo, K.K.R., Liu, C., Yu, M., Huang, W., Mohapatra, P.: Anomaly detection with graph convolutional networks for insider threat and fraud detection. In: IEEE Military Communications Conference (MILCOM), pp. 109\u2013114 (2019). https:\/\/doi.org\/10.1109\/MILCOM47813.2019.9020760","DOI":"10.1109\/MILCOM47813.2019.9020760"},{"key":"721_CR47","doi-asserted-by":"publisher","unstructured":"Keller, F., M\u00fcller, E., Wixler, A., B\u00f6hm, K.: Flexible and adaptive subspace search for outlier analysis. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 1381\u20131390 (2013). https:\/\/doi.org\/10.1145\/2505515.2505560","DOI":"10.1145\/2505515.2505560"},{"key":"721_CR48","volume-title":"Programming Massively Parallel Processors: A Hands-on Approach","author":"DB Kirk","year":"2016","unstructured":"Kirk, D.B., Hwu, W.M.W.: Programming Massively Parallel Processors: A Hands-on Approach, 3rd edn. Morgan Kaufmann, Burlington (2016)","edition":"3"},{"key":"721_CR49","unstructured":"Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Proceedings of the 24rd International Conference on Very Large Data Bases, pp. 392\u2013403 (1998)"},{"key":"721_CR50","unstructured":"Knorr, E.M., Ng, R.T.: Finding intensional knowledge of distance-based outliers. In: Proceedings of the 25th International Conference on Very Large Data Bases, pp. 211\u2013222 (1999)"},{"key":"721_CR51","unstructured":"Kopp, M., Holena, M.: Evaluation of association rules extracted during anomaly explanation. In: ITAT (2015)"},{"key":"721_CR52","unstructured":"Kopp, M., Pevn\u00fd, T., Holena, M.: Interpreting and clustering outliers with sapling random forests. In: Information Technologies\u2014Applications and Theory (2014)"},{"key":"721_CR53","doi-asserted-by":"publisher","unstructured":"Koutris, P., Salihoglu, S., Suciu, D.: Algorithmic aspects of parallel data processing (2018). https:\/\/doi.org\/10.1561\/1900000055","DOI":"10.1561\/1900000055"},{"key":"721_CR54","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Krager, P., Schubert, E., Zimek, A.: Interpreting and unifying outlier scores. In: Proceedings of the SIAM International Conference on Data Mining, pp. 13\u201324 (2011)","DOI":"10.1137\/1.9781611972818.2"},{"key":"721_CR55","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Kr\u00f6ger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: Advances in Knowledge Discovery and Data Mining, pp. 831\u2013838 (2009)","DOI":"10.1007\/978-3-642-01307-2_86"},{"key":"721_CR56","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.scitotenv.2019.02.085","volume":"664","author":"C Leigh","year":"2019","unstructured":"Leigh, C., Alsibai, O., Hyndman, R.J., Kandanaarachchi, S., King, O.C., McGree, J.M., Neelamraju, C., Strauss, J., Talagala, P.D., Turner, R.D.R., Mengersen, K.L., Peterson, E.E.: A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. Sci. Total Environ. 664, 885\u2013898 (2019)","journal-title":"Sci. Total Environ."},{"key":"721_CR57","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420\u2013429 (2007)","DOI":"10.1145\/1281192.1281239"},{"key":"721_CR58","doi-asserted-by":"publisher","first-page":"29356","DOI":"10.1109\/ACCESS.2021.3058809","volume":"9","author":"D Li","year":"2021","unstructured":"Li, D., Guo, H., Wang, Z., Zheng, Z.: Unsupervised fake news detection based on autoencoder. IEEE Access 9, 29356\u201329365 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3058809","journal-title":"IEEE Access"},{"issue":"5","key":"721_CR59","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.3390\/s20051461","volume":"20","author":"HZ Li","year":"2020","unstructured":"Li, H.Z., Boulanger, P.: A survey of heart anomaly detection using ambulatory electrocardiogram (ECG). Sensors 20(5), 1461 (2020)","journal-title":"Sensors"},{"key":"721_CR60","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422 (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"721_CR61","doi-asserted-by":"crossref","unstructured":"Liu, N., Shin, D., Hu, X.: Contextual outlier interpretation. In: 27th International Joint Conference on Artificial Intelligence (2018)","DOI":"10.24963\/ijcai.2018\/341"},{"key":"721_CR62","doi-asserted-by":"publisher","unstructured":"Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xing, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1010\u20131018 (2011). https:\/\/doi.org\/10.1145\/2020408.2020571","DOI":"10.1145\/2020408.2020571"},{"key":"721_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.apgeochem.2020.104710","author":"Z Luo","year":"2020","unstructured":"Luo, Z., Xiong, Y., Zuo, R.: Recognition of geochemical anomalies using a deep variational autoencoder network. Appl. Geochem. (2020). https:\/\/doi.org\/10.1016\/j.apgeochem.2020.104710","journal-title":"Appl. Geochem."},{"key":"721_CR64","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1007\/s10618-018-0585-7","volume":"32","author":"M Macha","year":"2018","unstructured":"Macha, M., Akoglu, L.: Explaining anomalies in groups with characterizing subspace rules. Data Min. Knowl. Discov. 32, 1444\u20131480 (2018)","journal-title":"Data Min. Knowl. Discov."},{"key":"721_CR65","first-page":"49","volume":"2","author":"PC Mahalanobis","year":"1936","unstructured":"Mahalanobis, P.C.: On the generalized distance in statistics. Proc. Natl. Inst. Sci. 2, 49\u201355 (1936)","journal-title":"Proc. Natl. Inst. Sci."},{"key":"721_CR66","doi-asserted-by":"publisher","DOI":"10.1177\/1475921720924601","author":"J Mao","year":"2021","unstructured":"Mao, J., Wang, H., Spencer, B.F.: Toward data anomaly detection for automated structural health monitoring: exploiting generative adversarial nets and autoencoders. Struct. Health Monit. (2021). https:\/\/doi.org\/10.1177\/1475921720924601","journal-title":"Struct. Health Monit."},{"key":"721_CR67","unstructured":"Meliou, A., Gatterbauer, W., Halpern, J.Y., Koch, C., Moore, K.F., Suciu, D.: Causality in databases. IEEE Data Eng. Bull. 33(EPFL-ARTICLE-165841) (2010)"},{"key":"721_CR68","doi-asserted-by":"crossref","unstructured":"Micenkov\u00e1, B., Ng, R.T., Dang, X.H., Assent, I.: Explaining outliers by subspace separability. In: IEEE 13th International Conference on Data Mining, pp. 518\u2013527 (2013)","DOI":"10.1109\/ICDM.2013.132"},{"issue":"1","key":"721_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2808691","volume":"48","author":"A Milenkoski","year":"2015","unstructured":"Milenkoski, A., Vieira, M., Kounev, S., Avritzer, A., Payne, B.D.: Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput. Surv. 48(1), 1\u201341 (2015). https:\/\/doi.org\/10.1145\/2808691","journal-title":"ACM Comput. Surv."},{"key":"721_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. (2019). https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","journal-title":"Artif. Intell."},{"key":"721_CR71","unstructured":"Molnar, C.: A guide for making black box models explainable. Leanpub. https:\/\/leanpub.com\/interpretable-machine-learning"},{"key":"721_CR72","unstructured":"Myrtakis Nikolaos Christophides\u00a0Vassilis, S.E.: A comparative evaluation of anomaly explanation algorithms. In: 24th International Conference on Extending Database Technology, pp. 97\u2013108. OpenProceedings.org (2021)"},{"key":"721_CR73","doi-asserted-by":"publisher","unstructured":"Ng, R.: Outlier detection in personalized medicine. In: Proceedings of the ACM SIGKDD workshop on outlier detection and description, p.\u00a07. Association for Computing Machinery (2013). https:\/\/doi.org\/10.1145\/2500853.2500856","DOI":"10.1145\/2500853.2500856"},{"key":"721_CR74","doi-asserted-by":"crossref","unstructured":"Niu, Z., Shi, S., Sun, J., He, X.: A survey of outlier detection methodologies and their applications. In: Artificial Intelligence and Computational Intelligence. Lecture Notes in Computer Science, pp. 380\u2013387. Springer, Berlin (2011)","DOI":"10.1007\/978-3-642-23881-9_50"},{"key":"721_CR75","unstructured":"NVIDIA: Geforce GPUs. https:\/\/www.nvidia.com\/en-gb\/graphics-cards\/ (2021). Accessed 4 Oct 2021"},{"issue":"6","key":"721_CR76","doi-asserted-by":"publisher","first-page":"745","DOI":"10.2197\/ipsjjip.23.745","volume":"23","author":"T Otsuka","year":"2015","unstructured":"Otsuka, T., Torii, Y., Ito, T.: Anomaly weather information detection using wireless pressure-sensor grid. J. Inf. Process. 23(6), 745\u2013752 (2015). https:\/\/doi.org\/10.2197\/ipsjjip.23.745","journal-title":"J. Inf. Process."},{"key":"721_CR77","unstructured":"Pascual, A.L., Kyle, M., Van Aleia, D.: Overcoming False Positives: Saving the Sale and the Customer Relationship. Technical report, Javelin strategy and research reports (2015)"},{"key":"721_CR78","doi-asserted-by":"crossref","unstructured":"Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Lecture Notes in Computer Science, pp. 398\u2013416 (1999)","DOI":"10.1007\/3-540-49257-7_25"},{"key":"721_CR79","unstructured":"PNPOLY-Point Inclusion in Polygon Test W. Randolph Franklin (WRF). https:\/\/wrf.ecse.rpi.edu\/\/Research\/Short_Notes\/pnpoly.html. Accessed 4 Oct 2021"},{"key":"721_CR80","doi-asserted-by":"publisher","unstructured":"Pozo-P\u00e9rez, J.A., Medina, D., Herrera-Pinz\u00f3n, I., He\u00dfelbarth, A., Ziebold, R.: Robust outlier mitigation in multi-constellation GNSS positioning for waterborne applications. In: Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, pp. 1330\u20131343 (2017). https:\/\/doi.org\/10.33012\/2017.14936","DOI":"10.33012\/2017.14936"},{"key":"721_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2020.104869","author":"S Russo","year":"2020","unstructured":"Russo, S., L\u00fcrig, M., Hao, W., Matthews, B., Villez, K.: Active learning for anomaly detection in environmental data. Environ. Model. Softw. (2020). https:\/\/doi.org\/10.1016\/j.envsoft.2020.104869","journal-title":"Environ. Model. Softw."},{"key":"721_CR82","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1145\/2594473.2594479","volume":"15","author":"MS Sadik","year":"2014","unstructured":"Sadik, M.S., Gruenwald, L.: Research issues in outlier detection for data streams. SIGKDD Explor. 15, 33\u201340 (2014)","journal-title":"SIGKDD Explor."},{"key":"721_CR83","doi-asserted-by":"crossref","unstructured":"Schubert, E., Wojdanowski, R., Zimek, A., Kriegel, H.P.: On evaluation of outlier rankings and outlier scores. In: Proceedings of the SIAM International Conference on Data Mining (2012)","DOI":"10.1137\/1.9781611972825.90"},{"issue":"2","key":"721_CR84","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s10462-012-9370-y","volume":"43","author":"N Shahid","year":"2015","unstructured":"Shahid, N., Naqvi, I.H., Qaisar, S.B.: Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey. Artif. Intell. Rev. 43(2), 193\u2013228 (2015). https:\/\/doi.org\/10.1007\/s10462-012-9370-y","journal-title":"Artif. Intell. Rev."},{"key":"721_CR85","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1561\/2200000018","volume":"4","author":"S Shalev-Shwartz","year":"2012","unstructured":"Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Mach. Learn. 4, 107\u2013194 (2012)","journal-title":"Found. Trends Mach. Learn."},{"key":"721_CR86","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3230666","volume":"13","author":"MA Siddiqui","year":"2019","unstructured":"Siddiqui, M.A., Fern, A., Dietterich, T.G., Wong, W.K.: Sequential feature explanations for anomaly detection. ACM Trans. Knowl. Discov. Data 13, 1\u201322 (2019)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"721_CR87","doi-asserted-by":"crossref","unstructured":"Siddiqui, M.A., Fern, A., Dietterich, T.G., Wright, R., Theriault, A., Archer, D.W.: Feedback-Guided Anomaly Discovery via Online Optimization. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)","DOI":"10.1145\/3219819.3220083"},{"key":"721_CR88","doi-asserted-by":"crossref","unstructured":"Silveira, F., Diot, C.: Urca: Pulling out anomalies by their root causes. In: 2010 Proceedings IEEE INFOCOM, pp. 1\u20139 (2010)","DOI":"10.1109\/INFCOM.2010.5462151"},{"key":"721_CR89","doi-asserted-by":"publisher","unstructured":"Song, F., Diao, Y., Read, J., Stiegler, A., Bifet, A.: Exad: A system for explainable anomaly detection on big data traces. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1435\u20131440 (2018). https:\/\/doi.org\/10.1109\/ICDMW.2018.00204","DOI":"10.1109\/ICDMW.2018.00204"},{"key":"721_CR90","doi-asserted-by":"crossref","unstructured":"Takeishi, N.: Shapley values of reconstruction errors of PCA for explaining anomaly detection. In: 2019 International Conference on Data Mining Workshops, pp. 793\u2013798 (2019)","DOI":"10.1109\/ICDMW.2019.00117"},{"key":"721_CR91","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58, 267\u2013288 (1996)","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"issue":"2","key":"721_CR92","doi-asserted-by":"publisher","first-page":"141","DOI":"10.14778\/3425879.3425885","volume":"14","author":"L Tran","year":"2021","unstructured":"Tran, L., Mun, M.Y., Shahabi, C.: Real-time distance-based outlier detection in data streams. Proc. VLDB Endow. 14(2), 141\u2013153 (2021). https:\/\/doi.org\/10.14778\/3425879.3425885","journal-title":"Proc. VLDB Endow."},{"issue":"1","key":"721_CR93","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-015-0030-3","volume":"2","author":"CW Tsai","year":"2015","unstructured":"Tsai, C.W., Lai, C.F., Chao, H.C., Vasilakos, A.: Big data analytics: a survey. J. Big Data 2(1), 1\u201332 (2015)","journal-title":"J. Big Data"},{"key":"721_CR94","unstructured":"UCI Machine Learning Repository. https:\/\/archive.ics.uci.edu\/ml\/index.php. Accessed 4 Oct 2021"},{"key":"721_CR95","unstructured":"US7346471B2\u2014Web Data Outlier Detection and Mitigation. https:\/\/patents.google.com\/patent\/US7346471. Accessed 4 Oct 2021"},{"key":"721_CR96","unstructured":"Valko, M., Cooper, G., Seybert, A.L., Visweswaran, S., Saul, M., Hauskrecht, M.: Conditional anomaly detection methods for patient-management alert systems. In: Proceedings of the International Conference on Machine Learning. International Conference on Machine Learning, vol. 2008 (2008)"},{"key":"721_CR97","doi-asserted-by":"crossref","unstructured":"Viswanathan, K., Lakshminarayan, C., Talwar, V., Wang, C., Macdonald, G., Satterfield, W.: Ranking anomalies in data centers. In: 2012 IEEE Network Operations and Management Symposium, pp. 79\u201387 (2012)","DOI":"10.1109\/NOMS.2012.6211885"},{"key":"721_CR98","doi-asserted-by":"crossref","unstructured":"Wedge, R., Kanter, J.M., Veeramachaneni, K., Rubio, S.M., Perez, S.I.: Solving the false positives problem in fraud prediction using automated feature engineering. In: Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, pp. 372\u2013388. Springer International Publishing (2019)","DOI":"10.1007\/978-3-030-10997-4_23"},{"key":"721_CR99","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/COMST.2014.2336610","volume":"17","author":"DJ Weller-Fahy","year":"2015","unstructured":"Weller-Fahy, D.J., Borghetti, B.J., Sodemann, A.A.: A survey of distance and similarity measures used within network intrusion anomaly detection. IEEE Commun. Surv. Tutor. 17, 70\u201391 (2015)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"721_CR100","doi-asserted-by":"crossref","unstructured":"Xing, L., Wang, W., Xue, G., Yu, H., Chi, X., Dai, W.: Discovering traffic outlier causal relationship based on anomalous DAG. In: Advances in Swarm and Computational Intelligence, pp. 71\u201380 (2015)","DOI":"10.1007\/978-3-319-20472-7_8"},{"issue":"1","key":"721_CR101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980765.2980767","volume":"18","author":"R Yu","year":"2016","unstructured":"Yu, R., Qiu, H., Wen, Z., Lin, C., Liu, Y.: A survey on social media anomaly detection. ACM SIGKDD Explor. Newsl. 18(1), 1\u201314 (2016). https:\/\/doi.org\/10.1145\/2980765.2980767","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"721_CR102","unstructured":"Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: Cluster computing with working sets. In: 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010 (2010)"},{"key":"721_CR103","doi-asserted-by":"publisher","unstructured":"Zemicheal, T., Dietterich, T.G.: Anomaly detection in the presence of missing values for weather data quality control. In: Proceedings of the 2019 Conference on Computing and Sustainable Societies (2019). https:\/\/doi.org\/10.1145\/3314344.3332490","DOI":"10.1145\/3314344.3332490"},{"key":"721_CR104","doi-asserted-by":"publisher","unstructured":"Zhang, H., Diao, Y., Meliou, A.: EXstream: explaining anomalies in event stream monitoring. In: 20th International Conference on Extending Database Technology (2017). https:\/\/doi.org\/10.5441\/002\/edbt.2017.15","DOI":"10.5441\/002\/edbt.2017.15"},{"key":"721_CR105","doi-asserted-by":"publisher","first-page":"e2","DOI":"10.4108\/trans.sis.2013.01-03.e2","volume":"1","author":"J Zhang","year":"2013","unstructured":"Zhang, J.: Advancements of outlier detection: a survey. EAI Endorsed Trans. Scalable Inf. Syst. 1, e2 (2013)","journal-title":"EAI Endorsed Trans. Scalable Inf. Syst."},{"key":"721_CR106","first-page":"813","volume-title":"A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data","author":"K Zhang","year":"2009","unstructured":"Zhang, K., Hutter, M., Jin, H.: A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data, pp. 813\u2013822. Springer, Berlin (2009)"},{"key":"721_CR107","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11161","volume":"5","author":"A Zimek","year":"2012","unstructured":"Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5, 363\u2013387 (2012)","journal-title":"Stat. Anal. Data Min."}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-021-00721-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-021-00721-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-021-00721-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:20:57Z","timestamp":1744154457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-021-00721-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,26]]},"references-count":107,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["721"],"URL":"https:\/\/doi.org\/10.1007\/s00778-021-00721-1","relation":{},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"value":"1066-8888","type":"print"},{"value":"0949-877X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,26]]},"assertion":[{"value":"21 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}