{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T14:58:37Z","timestamp":1766847517118},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T00:00:00Z","timestamp":1559520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T00:00:00Z","timestamp":1559520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Technical Research Centre of Finland"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s41060-019-00191-3","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T22:16:59Z","timestamp":1559600219000},"page":"285-314","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Unsupervised online detection and prediction of outliers in streams of sensor data"],"prefix":"10.1007","volume":"9","author":[{"given":"Niko","family":"Reunanen","sequence":"first","affiliation":[]},{"given":"Tomi","family":"R\u00e4ty","sequence":"additional","affiliation":[]},{"given":"Juho J.","family":"Jokinen","sequence":"additional","affiliation":[]},{"given":"Tyler","family":"Hoyt","sequence":"additional","affiliation":[]},{"given":"David","family":"Culler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,3]]},"reference":[{"key":"191_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.: On abnormality detection in spuriously populated data streams. In: Proceedings of the ACM SIAM Conference on Data Mining, pp. 80\u201391 (2005)","DOI":"10.1137\/1.9781611972757.8"},{"key":"191_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal, C., Zhao, Y., Yu, P.: Outlier detection in graph streams. In: Proceedings of the 27th International Conference on Data Engineering, pp. 399\u2013409 (2011)","DOI":"10.1109\/ICDE.2011.5767885"},{"issue":"2","key":"191_CR3","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s10618-009-0159-9","volume":"20","author":"F Angiulli","year":"2010","unstructured":"Angiulli, F., Fassetti, F.: Distance-based outlier queries in data streams: the novel task and algorithms. Data Min. Knowl. Dis. 20(2), 290\u2013324 (2010)","journal-title":"Data Min. Knowl. Dis."},{"key":"191_CR4","doi-asserted-by":"crossref","unstructured":"Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, Springer-Verlag, London, UK, PKDD \u201902, pp. 15\u201326 (2002)","DOI":"10.1007\/3-540-45681-3_2"},{"issue":"6","key":"191_CR5","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1145\/293347.293348","volume":"45","author":"S Arya","year":"1998","unstructured":"Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891\u2013923 (1998)","journal-title":"J. ACM"},{"key":"191_CR6","doi-asserted-by":"crossref","unstructured":"Assent, I., Kranen, P., Baldauf, C., Seidl, T.: Anyout: Anytime outlier detection on streaming data. In: Proceedings of the 17th International Conference on Database Systems for Advanced Applications, Springer-Verlag, Berlin, Heidelberg, DASFAA\u201912, pp. 228\u2013242 (2012)","DOI":"10.1007\/978-3-642-29038-1_18"},{"key":"191_CR7","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804779","volume-title":"Bayesian Reasoning and Machine Learning","author":"D Barber","year":"2012","unstructured":"Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, New York (2012)"},{"key":"191_CR8","doi-asserted-by":"crossref","unstructured":"Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, vol. 7700, Springer, Berlin, pp. 437\u2013478 (2012)","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"191_CR9","unstructured":"Bengio, Y., Yao, L., Alain, G., Vincent P.: Generalized Denoising Auto-Encoders as Generative Models. Advances in Neural Information Processing Systems (2013)"},{"key":"191_CR10","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601\u20131604 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"191_CR11","doi-asserted-by":"crossref","unstructured":"Bifet, A., de\u00a0Francisci\u00a0Morales, G., Read, J., Holmes, G., Pfahringer, B.: Efficient online evaluation of big data stream classifiers. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201915, pp. 59\u201368 (2015)","DOI":"10.1145\/2783258.2783372"},{"key":"191_CR12","volume-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Secaucus (2006)"},{"issue":"9","key":"191_CR13","first-page":"142","volume":"17","author":"L Bottou","year":"1998","unstructured":"Bottou, L.: Online learning and stochastic approximations. On-Line Learn. Neural Netw. 17(9), 142 (1998)","journal-title":"On-Line Learn. Neural Netw."},{"key":"191_CR14","doi-asserted-by":"crossref","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT\u20192010. Springer, pp. 177\u2013186 (2010)","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"191_CR15","doi-asserted-by":"crossref","unstructured":"Bouguessa, M.: Modeling outlier score distributions. In: International Conference on Advanced Data Mining and Applications. Springer, pp. 713\u2013725 (2012)","DOI":"10.1007\/978-3-642-35527-1_59"},{"issue":"5","key":"191_CR16","first-page":"01","volume":"2","author":"O Braimah","year":"2014","unstructured":"Braimah, O., Osanaiye, P., Omaku, P., Saheed, Y., Eshimokhai, S.: On the use of exponentially weighted moving average (ewma) control chart in monitoring road traffic crashes. Int. J. Math. Stat. Invent. (IJMSI) 2(5), 01\u201309 (2014)","journal-title":"Int. J. Math. Stat. Invent. (IJMSI)"},{"key":"191_CR17","doi-asserted-by":"crossref","unstructured":"Breunig, M., Kriegel, H.P., Ng, R., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the International Conference on Management of Data, ACM, New York, NY, USA, SIGMOD \u201900, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"issue":"6","key":"191_CR18","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.datak.2010.02.003","volume":"69","author":"G Bruno","year":"2010","unstructured":"Bruno, G., Garza, P.: TOD: temporal outlier detection by using quasi-functional temporal dependencies. Data Knowl. Eng. 69(6), 619\u2013639 (2010)","journal-title":"Data Knowl. Eng."},{"key":"191_CR19","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA. KDD \u201906, pp. 535\u2013541 (2006)","DOI":"10.1145\/1150402.1150464"},{"key":"191_CR20","doi-asserted-by":"crossref","unstructured":"Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., Rundensteiner, E.: Scalable distance-based outlier detection over high-volume data streams. In: Proceedings of the 30th International Conference on Data Engineering, ICDE, pp. 76\u201387 (2014)","DOI":"10.1109\/ICDE.2014.6816641"},{"issue":"4","key":"191_CR21","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s41060-018-0116-z","volume":"5","author":"F Carcillo","year":"2018","unstructured":"Carcillo, F., Le Borgne, Y.A., Caelen, O., Bontempi, G.: Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization. Int. J. Data Sci. Anal. 5(4), 285\u2013300 (2018)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"3","key":"191_CR22","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(3), 15:1\u201315:58 (2009)","journal-title":"ACM Comput. Surv."},{"key":"191_CR23","doi-asserted-by":"crossref","unstructured":"Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM International Conference on Data Mining, SIAM, pp. 90\u201398 (2017)","DOI":"10.1137\/1.9781611974973.11"},{"key":"191_CR24","unstructured":"Cho, K., Raiko, T., Ilin, A.: Enhanced gradient and adaptive learning rate for training restricted boltzmann machines. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28\u2013July 2, 2011, pp. 105\u2013112 (2011)"},{"key":"191_CR25","unstructured":"Dawson-Haggerty, S.: sMAP 2.0 Plotting Engine. http:\/\/www.openbms.org\/plot\/ (2015). Accessed 2 Feb 2015"},{"key":"191_CR26","doi-asserted-by":"crossref","unstructured":"Dawson-Haggerty, S., Jiang, X., Tolle, G., Ortiz, J., Culler, D.: sMAP: a simple measurement and actuation profile for physical information. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ACM, New York, NY, USA, SenSys \u201910, pp. 197\u2013210 (2010)","DOI":"10.1145\/1869983.1870003"},{"key":"191_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0150-x","author":"J De Stefani","year":"2018","unstructured":"De Stefani, J., Le Borgne, Y.A., Caelen, O., Hattab, D., Bontempi, G.: Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. Int. J. Data Sci. Anal. (2018). https:\/\/doi.org\/10.1007\/s41060-018-0150-x","journal-title":"Int. J. Data Sci. Anal."},{"key":"191_CR28","unstructured":"Dokas, P., Ertoz, L., Kumar, V., Lazarevic, A., Srivastava, J., Tan, P.: Data mining for network intrusion detection. In: Proceedings of the NSF workshop on next generation data mining, pp. 21\u201330 (2002)"},{"issue":"1","key":"191_CR29","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1111\/coin.12146","volume":"34","author":"Y Dong","year":"2018","unstructured":"Dong, Y., Japkowicz, N.: Threaded ensembles of autoencoders forstream learning. Comput. Intell. 34(1), 261\u2013281 (2018). https:\/\/doi.org\/10.1111\/coin.12146","journal-title":"Comput. Intell."},{"issue":"8","key":"191_CR30","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"issue":"5","key":"191_CR31","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1109\/TNNLS.2013.2283456","volume":"25","author":"H Ferdowsi","year":"2014","unstructured":"Ferdowsi, H., Jagannathan, S., Zawodniok, M.: An online outlier identification and removal scheme for improving fault detection performance. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 908\u2013919 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"191_CR32","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.datak.2015.07.010","volume":"98","author":"R Fileto","year":"2015","unstructured":"Fileto, R., May, C., Renso, C., Pelekis, N., Klein, D., Theodoridis, Y.: The Baquara2 knowledge-based framework for semantic enrichment and analysis of movement data. Data Knowl. Eng. 98, 104\u2013122 (2015)","journal-title":"Data Knowl. Eng."},{"key":"191_CR33","unstructured":"Finch, T.: Incremental calculation of weighted mean and variance. Technical report, University of Cambridge (2009)"},{"issue":"12","key":"191_CR34","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/j.datak.2011.07.002","volume":"70","author":"F Folino","year":"2011","unstructured":"Folino, F., Greco, G., Guzzo, A., Pontieri, L.: Mining usage scenarios in business processes: outlier-aware discovery and run-time prediction. Data Knowl. Eng. 70(12), 1005\u20131029 (2011)","journal-title":"Data Knowl. Eng."},{"key":"191_CR35","doi-asserted-by":"crossref","unstructured":"Fong, S., Nannan, Z., Wong, R., Yang, X.: Rare events forecasting using a residual-feedback GMDH neural network. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops, ICDMW, pp. 464\u2013473 (2012)","DOI":"10.1109\/ICDMW.2012.67"},{"key":"191_CR36","unstructured":"Franke, C., Gertz, M.: Detection and exploration of outlier regions in sensor data streams. In: Proceedings of the IEEE International Conference on Data Mining Workshops, ICDMW, pp. 375\u2013384"},{"key":"191_CR37","doi-asserted-by":"crossref","unstructured":"Georgiadis, D., Kontaki, M., Gounaris, A., Papadopoulos, A., Tsichlas, K., Manolopoulos, Y.: Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, SIGMOD \u201913, pp. 1061\u20131064 (2013)","DOI":"10.1145\/2463676.2463691"},{"issue":"3\u20134","key":"191_CR38","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s41060-016-0019-9","volume":"2","author":"A Giacometti","year":"2016","unstructured":"Giacometti, A., Soulet, A.: Anytime algorithm for frequent pattern outlier detection. Int. J. Data Sci. Anal. 2(3\u20134), 119\u2013130 (2016)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"9","key":"191_CR39","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2014","unstructured":"Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250\u20132267 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"191_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of Outliers","author":"D Hawkings","year":"1980","unstructured":"Hawkings, D.: Identification of Outliers. Chapman and Hall, London (1980)"},{"key":"191_CR41","first-page":"695","volume":"6","author":"A Hyv\u00e4rinen","year":"2005","unstructured":"Hyv\u00e4rinen, A.: Estimation of non-normalized statistical models by score matching. J. Mach. Learn. Res. 6, 695\u2013709 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"191_CR42","doi-asserted-by":"crossref","unstructured":"Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, ACM, New York, NY, USA, STOC \u201998, pp. 604\u2013613 (1998)","DOI":"10.1145\/276698.276876"},{"key":"191_CR43","unstructured":"Janssen, J., Huszar, F., Postma, E., van\u00a0den Herik, E.: Stochastic outlier selection (2012)"},{"issue":"5","key":"191_CR44","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1109\/TKDE.2018.2848902","volume":"31","author":"S Jian","year":"2019","unstructured":"Jian, S., Pang, G., Cao, L., Lu, K., Gao, H.: Cure: Flexible categorical data representation by hierarchical coupling learning. IEEE Trans. Knowl. Data Eng. 31(5), 853\u2013866 (2019). https:\/\/doi.org\/10.1109\/TKDE.2018.2848902","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"191_CR45","doi-asserted-by":"crossref","unstructured":"Kao, L., Huang, Y.: Association rules based algorithm for identifying outlier transactions in data stream. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 3209\u20133214 (2012)","DOI":"10.1109\/ICSMC.2012.6378285"},{"issue":"2","key":"191_CR46","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1093\/oxfordjournals.pan.a004868","volume":"9","author":"G King","year":"2001","unstructured":"King, G., Zeng, L.: Logistic regression in rare events data. Political Anal. 9(2), 137\u2013163 (2001)","journal-title":"Political Anal."},{"issue":"3\u20134","key":"191_CR47","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"E Knorr","year":"2000","unstructured":"Knorr, E., Ng, R., Tucakov, V.: Distance-based outliers: algorithms and applications. VLDB J. 8(3\u20134), 237\u2013253 (2000)","journal-title":"VLDB J."},{"key":"191_CR48","doi-asserted-by":"crossref","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A., Tsichlas, K., Manolopoulos, Y.: Continuous monitoring of distance-based outliers over data streams. In: Proceedings of the IEEE 27th International Conference on Data Engineering (ICDE), pp. 135\u2013146 (2011)","DOI":"10.1109\/ICDE.2011.5767923"},{"key":"191_CR49","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Hubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, KDD \u201908, pp. 444\u2013452 (2008)","DOI":"10.1145\/1401890.1401946"},{"key":"191_CR50","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Kr\u00f6ger, P., Schubert, E., Zimek, A.: Loop: local outlier probabilities. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, ACM, New York, NY, USA, CIKM \u201909, pp. 1649\u20131652 (2009)","DOI":"10.1145\/1645953.1646195"},{"key":"191_CR51","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Kr\u00f6ger, P., Schubert, E., Zimek, A.: Interpreting and unifying outlier scores. In: Proceedings of the SIAM International Conference on Data Mining, SIAM\/Omnipress, SDM, pp. 13\u201324 (2011)","DOI":"10.1137\/1.9781611972818.2"},{"issue":"7","key":"191_CR52","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TKDE.2012.99","volume":"25","author":"Y Lee","year":"2013","unstructured":"Lee, Y., Yeh, Y., Wang, Y.: Anomaly detection via online oversampling principal component analysis. IEEE Trans. Knowl. Data Eng. 25(7), 1460\u20131470 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"9","key":"191_CR53","doi-asserted-by":"publisher","first-page":"4321","DOI":"10.1109\/TIP.2017.2713048","volume":"26","author":"W Lu","year":"2017","unstructured":"Lu, W., Cheng, Y., Xiao, C., Chang, S., Huang, S., Liang, B., Huang, T.: Unsupervised sequential outlier detection with deep architectures. IEEE Trans. Image Process. 26(9), 4321\u20134330 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"191_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00401706.1990.10484583","volume":"32","author":"JM Lucas","year":"1990","unstructured":"Lucas, J.M., Saccucci, M.S.: Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1), 1\u201312 (1990)","journal-title":"Technometrics"},{"key":"191_CR55","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhang, P., Cao, Y., Guo, L.: Parallel auto-encoder for efficient outlier detection. In: Proceedings of the 2013 IEEE International Conference on Big Data, pp. 15\u201317 (2013)","DOI":"10.1109\/BigData.2013.6691791"},{"key":"191_CR56","doi-asserted-by":"crossref","unstructured":"Mai, J., Chuah, C., Sridharan, A., Ye, T., Zang, H.: Is sampled data sufficient for anomaly detection? In: Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, ACM, IMC \u201906, pp. 165\u2013176 (2006)","DOI":"10.1145\/1177080.1177102"},{"issue":"3","key":"191_CR57","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s41060-017-0044-3","volume":"3","author":"N Moniz","year":"2017","unstructured":"Moniz, N., Branco, P., Torgo, L.: Resampling strategies for imbalanced time series forecasting. Int. J. Data Sci. Anal. 3(3), 161\u2013181 (2017). https:\/\/doi.org\/10.1007\/s41060-017-0044-3","journal-title":"Int. J. Data Sci. Anal."},{"key":"191_CR58","volume-title":"Machine Learning: A Probabilistic Perspective","author":"K Murphy","year":"2012","unstructured":"Murphy, K.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)"},{"key":"191_CR59","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Ang, H., Gopalkrishnan, V.: Mining outliers with ensemble of heterogeneous detectors on random subspaces. In: Proceedings of the 15th International Conference on Database Systems for Advanced Applications, Springer-Verlag, Berlin, Heidelberg, DASFAA\u201910, pp. 368\u2013383 (2010)","DOI":"10.1007\/978-3-642-12026-8_29"},{"key":"191_CR60","doi-asserted-by":"publisher","unstructured":"Pang, G., Xu, H., Cao, L., Zhao, W.: Selective value coupling learning for detecting outliers in high-dimensional categorical data. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, ACM, New York, NY, USA, CIKM \u201917, pp. 807\u2013816. (2017). https:\/\/doi.org\/10.1145\/3132847.3132994","DOI":"10.1145\/3132847.3132994"},{"key":"191_CR61","doi-asserted-by":"publisher","unstructured":"Pang, G., Cao, L., Chen, L., Liu, H.: xLearning representations of ultrahigh-dimensional data for random distance-based outlier detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, New York, NY, USA, KDD \u201918, pp. 2041\u20132050. (2017). https:\/\/doi.org\/10.1145\/3219819.3220042","DOI":"10.1145\/3219819.3220042"},{"key":"191_CR62","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.datak.2013.05.002","volume":"87","author":"L Pang","year":"2013","unstructured":"Pang, L., Chawla, S., Liu, W., Zheng, Y.: On detection of emerging anomalous traffic patterns using GPS data. Data Knowl. Eng. 87, 357\u2013373 (2013)","journal-title":"Data Knowl. Eng."},{"key":"191_CR63","unstructured":"Papadimitriou, S., Kitagawa, H., Gibbons, P., Faloutsos, C.: Loci: Fast outlier detection using the local correlation integral. In: Proceedings of the International Conference on Data Engineering, IEEE Computer Society, ICDE, pp. 315\u2013326 (2003)"},{"key":"191_CR64","series-title":"Lecture Notes in Computer Science","volume-title":"Neural Networks: Tricks of the Trade","author":"L Prechelt","year":"2012","unstructured":"Prechelt, L.: Early stopping\u2013but when? In: Montavon, G., Orr, G., M\u00fcller, K.R. (eds.) Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, vol. 7700. Springer, New York (2012)"},{"issue":"5","key":"191_CR65","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TSMCC.2013.2261984","volume":"44","author":"F Rasheed","year":"2014","unstructured":"Rasheed, F., Alhajj, R.: A framework for periodic outlier pattern detection in time-series sequences. IEEE Trans. Cybern. 44(5), 569\u2013582 (2014)","journal-title":"IEEE Trans. Cybern."},{"key":"191_CR66","doi-asserted-by":"crossref","unstructured":"Raza, H., Prasad, G., Li, Y.: EWMA based two-stage dataset shift-detection in non-stationary environments. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer, pp. 625\u2013635 (2013)","DOI":"10.1007\/978-3-642-41142-7_63"},{"key":"191_CR67","doi-asserted-by":"crossref","unstructured":"Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, ACM, New York, NY, USA, MLSDA\u201914, pp. 4:4\u20134:11 (2014)","DOI":"10.1145\/2689746.2689747"},{"issue":"2","key":"191_CR68","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1109\/TSMCB.2005.843274","volume":"35","author":"S Sarasamma","year":"2005","unstructured":"Sarasamma, S., Zhu, Q., Huff, J.: Hierarchical Kohonenen net for anomaly detection in network security. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 35(2), 302\u2013312 (2005)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B: Cybern."},{"key":"191_CR69","doi-asserted-by":"crossref","unstructured":"Schubert, E., Weiler, M., Kriegel, H.: Signitrend: Scalable detection of emerging topics in textual streams by hashed significance thresholds. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, KDD \u201914, pp. 871\u2013880 (2014)","DOI":"10.1145\/2623330.2623740"},{"issue":"2","key":"191_CR70","doi-asserted-by":"publisher","first-page":"97","DOI":"10.14778\/2732228.2732229","volume":"7","author":"F Schuhknecht","year":"2013","unstructured":"Schuhknecht, F., Jindal, A., Dittrich, J.: The uncracked pieces in database cracking. Proc. VLDB Endow. 7(2), 97\u2013108 (2013)","journal-title":"Proc. VLDB Endow."},{"key":"191_CR71","doi-asserted-by":"publisher","DOI":"10.1201\/9781420036268","volume-title":"Handbook of Parametric and Nonparametric Statistical Procedures","author":"DJ Sheskin","year":"2003","unstructured":"Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2003)"},{"key":"191_CR72","doi-asserted-by":"publisher","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 and Data Mining, ACM, New York, NY, USA, KDD \u201918, pp. 2200\u20132209. (2018). https:\/\/doi.org\/10.1145\/3219819.3220083","DOI":"10.1145\/3219819.3220083"},{"issue":"1","key":"191_CR73","first-page":"2567","volume":"13","author":"J Snoek","year":"2012","unstructured":"Snoek, J., Adams, R., Larochelle, H.: Nonparametric guidance of autoencoder representations using label information. J. Mach. Learn. Res. 13(1), 2567\u20132588 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"191_CR74","doi-asserted-by":"crossref","unstructured":"Souiden, I., Brahmi, Z., Toumi, H.: A survey on outlier detection in the context of stream mining: review of existing approaches and recommadations. In: International Conference on Intelligent Systems Design and Applications, Springer, pp. 372\u2013383 (2016)","DOI":"10.1007\/978-3-319-53480-0_37"},{"key":"191_CR75","unstructured":"Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd international conference on Very large data bases, pp. 187\u2013198 (2006)"},{"key":"191_CR76","doi-asserted-by":"crossref","unstructured":"Tao, Y., Pi, D.: Unifying density-based clustering and outlier detection. In: Proceedings of the Second International Workshop on Knowledge Discovery and Data Mining, WKDD, pp. 644\u2013647 (2009)","DOI":"10.1109\/WKDD.2009.127"},{"issue":"1","key":"191_CR77","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"D Tax","year":"2004","unstructured":"Tax, D., Duin, R.: Support vector data description. Mach. Learn. 54(1), 45\u201366 (2004)","journal-title":"Mach. Learn."},{"key":"191_CR78","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0145-7","author":"D Teffer","year":"2018","unstructured":"Teffer, D., Srinivasan, R., Ghosh, J.: Adahash: hashing-based scalable, adaptive hierarchical clustering of streaming data on mapreduce frameworks. Int. J. Data Sci. Anal. (2018). https:\/\/doi.org\/10.1007\/s41060-018-0145-7","journal-title":"Int. J. Data Sci. Anal."},{"key":"191_CR79","volume-title":"Pattern Recognition","author":"S Theodoridis","year":"2008","unstructured":"Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, London (2008)","edition":"4"},{"key":"191_CR80","doi-asserted-by":"crossref","unstructured":"Torgo, L., Ribeiro, R.: Predicting outliers. In: Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 447\u2013458 (2003)","DOI":"10.1007\/978-3-540-39804-2_40"},{"issue":"12","key":"191_CR81","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.14778\/2994509.2994526","volume":"9","author":"L Tran","year":"2016","unstructured":"Tran, L., Fan, L., Shahabi, C.: Distance-based outlier detection in data streams. Proc. VLDB Endow. 9(12), 1089\u20131100 (2016)","journal-title":"Proc. VLDB Endow."},{"issue":"2","key":"191_CR82","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s41060-018-0137-7","volume":"7","author":"H Trittenbach","year":"2019","unstructured":"Trittenbach, H., B\u00f6hm, K.: Dimension-based subspace search for outlier detection. Int. J. Data Sci. Anal. 7(2), 87\u2013101 (2019). https:\/\/doi.org\/10.1007\/s41060-018-0137-7","journal-title":"Int. J. Data Sci. Anal."},{"key":"191_CR83","unstructured":"Vilalta, R., Ma, S.: Predicting rare events in temporal domains. In: Proceedings of the IEEE International Conference On Data Mining, pp. 474\u2013481 (2002)"},{"key":"191_CR84","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"9","key":"191_CR85","doi-asserted-by":"publisher","first-page":"6225","DOI":"10.1016\/j.eswa.2010.02.102","volume":"37","author":"G Wang","year":"2010","unstructured":"Wang, G., Hao, J., Ma, J., Huang, L.: A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37(9), 6225\u20136232 (2010)","journal-title":"Expert Syst. Appl."},{"issue":"Part A","key":"191_CR86","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.datak.2015.06.012","volume":"100","author":"H Wang","year":"2015","unstructured":"Wang, H., Liu, R.: Hiding outliers into crowd: privacy-preserving data publishing with outliers. Data Knowl. Eng. 100(Part A), 94\u2013115 (2015)","journal-title":"Data Knowl. Eng."},{"key":"191_CR87","unstructured":"Weiss, G., Hirsh, H.: Learning to predict rare events in event sequences. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, pp. 359\u2013363 (1998)"},{"key":"191_CR88","doi-asserted-by":"crossref","unstructured":"Wu, Q., Ma, S.: Detecting outliers in sliding window over categorical data streams. In: Proceedings of the 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD, vol.\u00a03, pp. 1663\u20131667 (2011)","DOI":"10.1109\/FSKD.2011.6019780"},{"key":"191_CR89","doi-asserted-by":"publisher","unstructured":"Xu, H., Wang, Y., Cheng, L., Wang, Y., Ma, X.: Exploring a high-quality outlying feature value set for noise-resilient outlier detection in categorical data. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ACM, New York, NY, USA, CIKM \u201918, pp. 17\u201326. (2018). https:\/\/doi.org\/10.1145\/3269206.3271721","DOI":"10.1145\/3269206.3271721"},{"key":"191_CR90","doi-asserted-by":"crossref","unstructured":"Yang, D., Rundensteiner, E., Ward, M.: Neighbor-based pattern detection for windows over streaming data. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, ACM, New York, NY, USA, EDBT \u201909, pp. 529\u2013540 (2009)","DOI":"10.1145\/1516360.1516422"},{"key":"191_CR91","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0161-7","author":"H Zhang","year":"2018","unstructured":"Zhang, H., Nian, K., Coleman, T.F., Li, Y.: Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection. Int. J. Data Sci. Anal. (2018). https:\/\/doi.org\/10.1007\/s41060-018-0161-7","journal-title":"Int. J. Data Sci. Anal."},{"key":"191_CR92","doi-asserted-by":"publisher","unstructured":"Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. Data Min. Knowl. Discov. (2009). https:\/\/doi.org\/10.1007\/978-3-642-01307-2_84","DOI":"10.1007\/978-3-642-01307-2_84"},{"issue":"2","key":"191_CR93","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/SURV.2010.021510.00088","volume":"12","author":"Y Zhang","year":"2010","unstructured":"Zhang, Y., Meratnia, N., Havinga, P.: Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 12(2), 159\u2013170 (2010)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"191_CR94","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lehman, B., Ball, R., Mosesian, J., de\u00a0Palma, J.: Outlier detection rules for fault detection in solar photovoltaic arrays. In: Proceedings of the 2013 Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp 2913\u20132920 (2013)","DOI":"10.1109\/APEC.2013.6520712"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-019-00191-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41060-019-00191-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-019-00191-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T06:56:11Z","timestamp":1663570571000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41060-019-00191-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,3]]},"references-count":94,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["191"],"URL":"https:\/\/doi.org\/10.1007\/s41060-019-00191-3","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,3]]},"assertion":[{"value":"22 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The author declares that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}