{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T10:08:21Z","timestamp":1790417301010,"version":"4.1.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["KK.01.1.1.01.0009"],"award-info":[{"award-number":["KK.01.1.1.01.0009"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s10994-020-05905-4","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T17:02:40Z","timestamp":1599238960000},"page":"139-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Statistical hierarchical clustering algorithm for outlier detection in evolving data streams"],"prefix":"10.1007","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7350-8858","authenticated-orcid":false,"given":"Dalibor","family":"Krle\u017ea","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris","family":"Vrdoljak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Br\u010di\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"5905_CR1","unstructured":"Ackerman, M., & Dasgupta, S. (2014). Incremental clustering: The case for extra clusters. In Advances in Neural Information Processing Systems (pp. 307\u2013315)."},{"key":"5905_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-47534-9","volume-title":"Data streams: Models and algorithms","author":"CC Aggarwal","year":"2007","unstructured":"Aggarwal, C. C. (2007). Data streams: Models and algorithms (Vol. 31). New York: Springer."},{"key":"5905_CR3","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2003). A framework for clustering evolving data streams. In Proceedings of the 29th international conference on Very large data bases-Volume 29, VLDB Endowment (pp. 81\u201392).","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"issue":"2","key":"5905_CR4","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1198\/jcgs.2010.08111","volume":"19","author":"JP Baudry","year":"2010","unstructured":"Baudry, J. P., Raftery, A. E., Celeux, G., Lo, K., & Gottardo, R. (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2), 332\u2013353.","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"5905_CR5","doi-asserted-by":"crossref","unstructured":"Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. In Proceedings of the 2007 SIAM international conference on data mining, SIAM (pp. 443\u2013448).","DOI":"10.1137\/1.9781611972771.42"},{"issue":"1","key":"5905_CR6","first-page":"1","volume":"83","author":"A Bifet","year":"2018","unstructured":"Bifet, A., Read, J., Holmes, G., & Pfahringer, B. (2018). Streaming data mining with massive online analytics (MOA). Series in Machine Perception and Artificial Intelligence, 83(1), 1\u201325.","journal-title":"Series in Machine Perception and Artificial Intelligence"},{"key":"5905_CR7","unstructured":"Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (2017). mlrMBO: A modular framework for model-based optimization of expensive black-box functions. 1703.03373"},{"key":"5905_CR8","unstructured":"Bradley, P. S., Fayyad, U. M., Reina, C., et\u00a0al. (1998). Scaling clustering algorithms to large databases. In KDD-98 (pp. 9\u201315)."},{"key":"5905_CR9","doi-asserted-by":"crossref","unstructured":"Cao, F., Estert, M., Qian, W., & Zhou, A. (2006). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining, SIAM (pp. 328\u2013339).","DOI":"10.1137\/1.9781611972764.29"},{"key":"5905_CR10","doi-asserted-by":"crossref","unstructured":"Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., & Rundensteiner, E. A. (2014). Scalable distance-based outlier detection over high-volume data streams. In 2014 IEEE 30th international conference on data engineering (ICDE), IEEE (pp. 76\u201387).","DOI":"10.1109\/ICDE.2014.6816641"},{"key":"5905_CR11","doi-asserted-by":"crossref","unstructured":"Carnein, M., Assenmacher, D., & Trautmann, H. (2017). An empirical comparison of stream clustering algorithms. In Proceedings of the computing frontiers conference, ACM (pp. 361\u2013366).","DOI":"10.1145\/3075564.3078887"},{"key":"5905_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y., & Tu, L. (2007). Density-based clustering for real-time stream data. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM (pp. 133\u2013142).","DOI":"10.1145\/1281192.1281210"},{"key":"5905_CR13","unstructured":"Ciaccia, P., Patella, M., & Zezula, P. (1997). M-tree: An efficient access method for similarity search in metric spaces. In Proceedings of the 23rd VLDB conference, Athens, Greece, Citeseer (pp. 426\u2013435)."},{"key":"5905_CR14","volume-title":"Sphere packings, lattices and groups","author":"JH Conway","year":"2013","unstructured":"Conway, J. H., & Sloane, N. J. A. (2013). Sphere packings, lattices and groups (Vol. 290). New York: Springer."},{"key":"5905_CR15","doi-asserted-by":"crossref","unstructured":"Dasgupta, S. (1999). Learning mixtures of gaussians. In 40th annual symposium on Foundations of computer science, 1999, IEEE (pp. 634\u2013644).","DOI":"10.1109\/SFFCS.1999.814639"},{"issue":"1","key":"5905_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","volume":"50","author":"R De Maesschalck","year":"2000","unstructured":"De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1\u201318.","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"5905_CR17","first-page":"34","volume":"1","author":"B Desgraupes","year":"2017","unstructured":"Desgraupes, B. (2017). Clustering indices. University of Paris Ouest-Lab Modal\u2019X, 1, 34.","journal-title":"University of Paris Ouest-Lab Modal\u2019X"},{"key":"5905_CR18","unstructured":"Dua, D., & Graff, C. (2017). UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml."},{"issue":"1","key":"5905_CR19","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1080\/00031305.2017.1375990","volume":"72","author":"D Eddelbuettel","year":"2018","unstructured":"Eddelbuettel, D., & Balamuta, J. J. (2018). Extending R with C++: A brief introduction to Rcpp. The American Statistician, 72(1), 28\u201336. https:\/\/doi.org\/10.1080\/00031305.2017.1375990.","journal-title":"The American Statistician"},{"key":"5905_CR20","unstructured":"Ester, M., Kriegel, H. P., Sander, J., Xu, X., et\u00a0al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD-96, AAAI (pp. 226\u2013231)."},{"key":"5905_CR21","doi-asserted-by":"crossref","unstructured":"Fichtenberger, H., Gill\u00e9, M., Schmidt, M., Schwiegelshohn, C., & Sohler, C. (2013). BICO: BIRCH meets coresets for k-means clustering. In European symposium on Algorithms (pp. 481\u2013492). New York: Springer.","DOI":"10.1007\/978-3-642-40450-4_41"},{"key":"5905_CR22","doi-asserted-by":"publisher","DOI":"10.1201\/EBK1439826119","volume-title":"Knowledge discovery from data streams","author":"J Gama","year":"2010","unstructured":"Gama, J. (2010). Knowledge discovery from data streams. Boca Raton: CRC Press."},{"key":"5905_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-73679-4","volume-title":"Learning from data streams: Processing techniques in sensor networks","author":"J Gama","year":"2007","unstructured":"Gama, J., & Gaber, M. M. (2007). Learning from data streams: Processing techniques in sensor networks. New York: Springer."},{"key":"5905_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0162-0","volume-title":"An intermediate course in probability","author":"A Gut","year":"2009","unstructured":"Gut, A. (2009). An intermediate course in probability (2nd ed.). New York: Springer. https:\/\/doi.org\/10.1007\/978-1-4419-0162-0.","edition":"2"},{"issue":"6","key":"5905_CR25","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/TKDE.2016.2522412","volume":"28","author":"M Hahsler","year":"2016","unstructured":"Hahsler, M., & Bola\u00f1os, M. (2016). Clustering data streams based on shared density between micro-clusters. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1449\u20131461.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"14","key":"5905_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v076.i14","volume":"76","author":"M Hahsler","year":"2017","unstructured":"Hahsler, M., Bolanos, M., Forrest, J., et al. (2017). Introduction to stream: An extensible framework for data stream clustering research with r. Journal of Statistical Software, 76(14), 1\u201350.","journal-title":"Journal of Statistical Software"},{"key":"5905_CR27","unstructured":"Hahsler, M., Bolanos, M., & Forrest, J. (2018). streamMOA: Interface for MOA Stream Clustering Algorithms. https:\/\/CRAN.R-project.org\/package=streamMOA, R package version 1.1-4."},{"key":"5905_CR28","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-319-15934-8_5","volume-title":"Evolutionary multi-criterion optimization","author":"D Horn","year":"2015","unstructured":"Horn, D., Wagner, T., Biermann, D., Weihs, C., & Bischl, B. (2015). Model-based multi-objective optimization: Taxonomy, multi-point proposal, toolbox and benchmark. In A. Gaspar-Cunha, C. Henggeler Antunes, & C. C. Coello (Eds.), Evolutionary multi-criterion optimization (pp. 64\u201378). Cham: Springer."},{"issue":"2","key":"5905_CR29","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","volume":"11","author":"P Jaccard","year":"1912","unstructured":"Jaccard, P. (1912). The distribution of the flora in the alpine zone.1. New Phytologist, 11(2), 37\u201350. https:\/\/doi.org\/10.1111\/j.1469-8137.1912.tb05611.x.","journal-title":"New Phytologist"},{"key":"5905_CR30","unstructured":"Jacob, B., Guennebaud, G., et\u00a0al. (2013). Eigen: C++ template library for linear algebra."},{"issue":"3","key":"5905_CR31","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. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31(3), 264\u2013323.","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"8","key":"5905_CR32","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/2.781637","volume":"32","author":"G Karypis","year":"1999","unstructured":"Karypis, G., Han, E. H., & Kumar, V. (1999). Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8), 68\u201375.","journal-title":"Computer"},{"key":"5905_CR33","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.is.2015.07.006","volume":"55","author":"M Kontaki","year":"2016","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A. N., Tsichlas, K., & Manolopoulos, Y. (2016). Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Information Systems, 55, 37\u201353.","journal-title":"Information Systems"},{"issue":"2","key":"5905_CR34","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10115-010-0342-8","volume":"29","author":"P Kranen","year":"2011","unstructured":"Kranen, P., Assent, I., Baldauf, C., & Seidl, T. (2011). The clustree: Indexing micro-clusters for anytime stream mining. Knowledge and Information Systems, 29(2), 249\u2013272.","journal-title":"Knowledge and Information Systems"},{"key":"5905_CR35","unstructured":"Krishnamoorthy, A., & Menon, D. (2013). Matrix inversion using cholesky decomposition. In 2013 signal processing: Algorithms, architectures, arrangements, and applications (SPA), IEEE (pp. 70\u201372)."},{"issue":"1","key":"5905_CR36","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1037\/0033-2909.105.1.156","volume":"105","author":"T Micceri","year":"1989","unstructured":"Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105(1), 156.","journal-title":"Psychological Bulletin"},{"key":"5905_CR37","doi-asserted-by":"crossref","unstructured":"Moshtaghi, M., Leckie, C., & Bezdek, J. C. (2016). Online clustering of multivariate time-series. In Proceedings of the 2016 SIAM international conference on data mining, SIAM (pp. 360\u2013368).","DOI":"10.1137\/1.9781611974348.41"},{"issue":"3","key":"5905_CR38","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","volume":"45","author":"HL Nguyen","year":"2015","unstructured":"Nguyen, H. L., Woon, Y. K., & Ng, W. K. (2015). A survey on data stream clustering and classification. Knowledge and Information Systems, 45(3), 535\u2013569. https:\/\/doi.org\/10.1007\/s10115-014-0808-1.","journal-title":"Knowledge and Information Systems"},{"issue":"1","key":"5905_CR39","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1111\/j.1744-6570.2011.01239.x","volume":"65","author":"E O\u2019Boyle Jr","year":"2012","unstructured":"O\u2019Boyle, E, Jr., & Aguinis, H. (2012). The best and the rest: Revisiting the norm of normality of individual performance. Personnel Psychology, 65(1), 79\u2013119.","journal-title":"Personnel Psychology"},{"key":"5905_CR40","volume-title":"Numerical recipes 3rd edition: The art of scientific computing","author":"WH Press","year":"2007","unstructured":"Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical recipes 3rd edition: The art of scientific computing. Cambridge: Cambridge University Press."},{"key":"5905_CR41","first-page":"442","volume":"419","author":"EM Rasmussen","year":"1992","unstructured":"Rasmussen, E. M. (1992). Clustering algorithms. Information Retrieval: Data Structures & Algorithms, 419, 442.","journal-title":"Information Retrieval: Data Structures & Algorithms"},{"issue":"1","key":"5905_CR42","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1214\/aoms\/1177729893","volume":"21","author":"J Sherman","year":"1950","unstructured":"Sherman, J., & Morrison, W. J. (1950). Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. The Annals of Mathematical Statistics, 21(1), 124\u2013127.","journal-title":"The Annals of Mathematical Statistics"},{"issue":"1","key":"5905_CR43","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/2522968.2522981","volume":"46","author":"JA Silva","year":"2013","unstructured":"Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., de Carvalho, A. C., & Gama, J. (2013). Data stream clustering: A survey. ACM Computing Surveys (CSUR), 46(1), 13.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"5905_CR44","doi-asserted-by":"crossref","unstructured":"Solaimani, M., Iftekhar, M., Khan, L., & Thuraisingham, B. (2014). Statistical technique for online anomaly detection using spark over heterogeneous data from multi-source vmware performance data. In 2014 IEEE international conference on Big Data (Big Data), IEEE (pp. 1086\u20131094).","DOI":"10.1109\/BigData.2014.7004343"},{"issue":"3","key":"5905_CR45","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1080\/00401706.1962.10490022","volume":"4","author":"BP Welford","year":"1962","unstructured":"Welford, B. P. (1962). Note on a method for calculating corrected sums of squares and products. Technometrics, 4(3), 419\u2013420.","journal-title":"Technometrics"},{"issue":"106","key":"5905_CR46","first-page":"336","volume":"42","author":"MA Woodbury","year":"1950","unstructured":"Woodbury, M. A. (1950). Inverting modified matrices. Memorandum Report, 42(106), 336.","journal-title":"Memorandum Report"},{"key":"5905_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: an efficient data clustering method for very large databases. In ACM Sigmod Record, ACM (pp. 103\u2013114).","DOI":"10.1145\/235968.233324"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-020-05905-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-020-05905-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-020-05905-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T02:10:52Z","timestamp":1630721452000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-020-05905-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,4]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["5905"],"URL":"https:\/\/doi.org\/10.1007\/s10994-020-05905-4","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,4]]},"assertion":[{"value":"16 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}