{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:23:31Z","timestamp":1763202211260},"reference-count":107,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,1,21]],"date-time":"2019-01-21T00:00:00Z","timestamp":1548028800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Bus Inf Syst Eng"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s12599-019-00576-5","type":"journal-article","created":{"date-parts":[[2019,1,21]],"date-time":"2019-01-21T05:06:58Z","timestamp":1548047218000},"page":"277-297","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms"],"prefix":"10.1007","volume":"61","author":[{"given":"Matthias","family":"Carnein","sequence":"first","affiliation":[]},{"given":"Heike","family":"Trautmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,21]]},"reference":[{"key":"576_CR1","doi-asserted-by":"publisher","first-page":"2.4:2.1","DOI":"10.1145\/2133803.2184450","volume":"17","author":"MR Ackermann","year":"2012","unstructured":"Ackermann MR, M\u00e4rtens M, Raupach C, Swierkot K, Lammersen C, Sohler C (2012) StreamKM++: a clustering algorithm for data streams. J Exp Algorithmics 17:2.4:2.1\u20132.4:2.30","journal-title":"J Exp Algorithmics"},{"key":"576_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 CC (2007) Data streams: models and algorithms, vol 31. Springer, Berlin"},{"key":"576_CR3","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: Proceedings of the 29th international conference on very large data bases, volume\u00a029 of VLDB \u201903, VLDB Endowment, Berlin, pp 81\u201392","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"576_CR4","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Han J, Wang J, Yu PS (2004) A framework for projected clustering of high dimensional data streams. In: Proceedings of the thirtieth international conference on very large data bases, volume\u00a030 of VLDB \u201904, VLDB Endowment, Toronto, pp 852\u2013863","DOI":"10.1016\/B978-012088469-8.50075-9"},{"key":"576_CR5","doi-asserted-by":"crossref","unstructured":"Ali MH, Sundus A, Qaiser W, Ahmed Z, Halim Z (2011) Applicative implementation of D-stream clustering algorithm for the real-time data of telecom sector. In: International conference on computer networks and information technology, pp 293\u2013297","DOI":"10.1109\/ICCNIT.2011.6020884"},{"key":"576_CR6","unstructured":"Amini A, Wah TY (2011) Density micro-clustering algorithms on data streams: a review. In: Proceeding of the international multiconference of engineers and computer scientists (IMECS)"},{"key":"576_CR7","first-page":"275","volume-title":"A comparative study of density-based clustering algorithms on data streams: micro-clustering approaches","author":"A Amini","year":"2012","unstructured":"Amini A, Wah TY (2012) A comparative study of density-based clustering algorithms on data streams: micro-clustering approaches. Springer, US, Boston, pp 275\u2013287"},{"issue":"05","key":"576_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.4236\/jcc.2013.15005","volume":"01","author":"A Amini","year":"2013","unstructured":"Amini A, Wah TY (2013) LeaDen-Stream: a leader density-based clustering algorithm over evolving data stream. J Comput Commun 01(05):26\u201331","journal-title":"J Comput Commun"},{"key":"576_CR9","doi-asserted-by":"crossref","unstructured":"Amini A, Wah TY, Saybani MR, Yazdi SRAS (2011) A study of density-grid based clustering algorithms on data streams. In: Eighth international conference on fuzzy systems and knowledge discovery (FSKD) 3:1652\u20131656","DOI":"10.1109\/FSKD.2011.6019867"},{"key":"576_CR10","unstructured":"Amini A, Wah TY, Teh YW (2012) DENGRIS-Stream: a density-grid based clustering algorithm for evolving data streams over sliding window. In: Proceedings of the international conference on data mining and computer engineering, pp 206\u2013210"},{"key":"576_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/926020","volume":"2014","author":"A Amini","year":"2014","unstructured":"Amini A, Saboohi H, Wah TY, Herawan T (2014a) A fast density-based clustering algorithm for real-time internet of things stream. Sci World J 2014:1\u201311","journal-title":"Sci World J"},{"issue":"1","key":"576_CR12","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s11390-014-1416-y","volume":"29","author":"A Amini","year":"2014","unstructured":"Amini A, Wah TY, Saboohi H (2014b) On density-based data streams clustering algorithms: a survey. J Comput Sci Technol 29(1):116\u2013141","journal-title":"J Comput Sci Technol"},{"key":"576_CR13","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.jnca.2014.11.007","volume":"59","author":"A Amini","year":"2016","unstructured":"Amini A, Saboohi H, Herawan T, Wah TY (2016) MuDi-Stream: a multi density clustering algorithm for evolving data stream. J Netw Comput Appl 59:370\u2013385","journal-title":"J Netw Comput Appl"},{"key":"576_CR14","unstructured":"Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, SODA \u201907, Society for Industrial and Applied Mathematics, New Orleans, pp 1027\u20131035"},{"key":"576_CR15","doi-asserted-by":"crossref","unstructured":"Barbar\u00e1 D, Chen P (2000) Using the fractal dimension to cluster datasets. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201900, ACM, Boston, pp 260\u2013264","DOI":"10.1145\/347090.347145"},{"issue":"2","key":"576_CR16","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1022493416690","volume":"7","author":"D Barbar\u00e1","year":"2003","unstructured":"Barbar\u00e1 D, Chen P (2003) Using self-similarity to cluster large data sets. Data Min Knowl Discov 7(2):123\u2013152","journal-title":"Data Min Knowl Discov"},{"key":"576_CR17","first-page":"125","volume":"2","author":"A Ben-Hur","year":"2001","unstructured":"Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125\u2013137","journal-title":"J Mach Learn Res"},{"key":"576_CR18","first-page":"217","volume-title":"When is \u201cnearest neighbor\u201d meaningful?","author":"K Beyer","year":"1999","unstructured":"Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is \u201cnearest neighbor\u201d meaningful? Springer, Berlin, pp 217\u2013235"},{"key":"576_CR19","first-page":"629","volume-title":"Exclusive and complete clustering of streams","author":"V Bhatnagar","year":"2007","unstructured":"Bhatnagar V, Kaur S (2007) Exclusive and complete clustering of streams. Springer, Berlin, pp 629\u2013638"},{"issue":"1","key":"576_CR20","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s10115-013-0659-1","volume":"41","author":"V Bhatnagar","year":"2014","unstructured":"Bhatnagar V, Kaur S, Chakravarthy S (2014) Clustering data streams using grid-based synopsis. Knowl Inf Syst 41(1):127\u2013152","journal-title":"Knowl Inf Syst"},{"key":"576_CR21","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601\u20131604","journal-title":"J Mach Learn Res"},{"key":"576_CR22","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/10654.001.0001","volume-title":"Machine learning for data streams with practical examples in MOA","author":"A Bifet","year":"2018","unstructured":"Bifet A, Gavald\u00e0 R, Holmes G, Pfahringer B (2018) Machine learning for data streams with practical examples in MOA. MIT Press, Cambridge"},{"key":"576_CR23","doi-asserted-by":"crossref","unstructured":"Bohm C, Kailing K, Kriegel H-P, Kroger P (2004) Density connected clustering with local subspace preferences. In: Proceedings of the fourth IEEE international conference on data mining, ICDM \u201904, IEEE Computer Society, Washington, DC, pp 27\u201334","DOI":"10.1109\/ICDM.2004.10087"},{"key":"576_CR24","first-page":"135","volume-title":"Data analysis, machine learning and knowledge discovery, studies in classification, data analysis, and knowledge organization","author":"M Bola\u00f1os","year":"2014","unstructured":"Bola\u00f1os M, Forrest J, Hahsler M (2014) Clustering large datasets using data stream clustering techniques. In: Spiliopoulou M, Schmidt-Thieme L, Janning R (eds) Data analysis, machine learning and knowledge discovery, studies in classification, data analysis, and knowledge organization. Springer, Berlin, pp 135\u2013143"},{"key":"576_CR25","unstructured":"Bradley PS, Fayyad U, Reina C (1998) Scaling clustering algorithms to large databases. In: Proceedings of the 4th international conference on knowledge discovery and data mining (KDD\u201998). AAAI Press, pp 9\u201315"},{"key":"576_CR26","doi-asserted-by":"crossref","unstructured":"Cao F, Ester M, Qian W, Zhou A (2006) Density-based clustering over an evolving data stream with noise. In: Conference on data mining (SIAM \u201906), pp 328\u2013339","DOI":"10.1137\/1.9781611972764.29"},{"key":"576_CR27","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.bdr.2018.05.005","volume":"14","author":"M Carnein","year":"2018","unstructured":"Carnein M, Trautmann H (2018) evoStream\u2014evolutionary stream clustering utilizing idle times. Big Data Res 14:101\u2013111. \n                    https:\/\/doi.org\/10.1016\/j.bdr.2018.05.005","journal-title":"Big Data Res"},{"key":"576_CR28","doi-asserted-by":"crossref","unstructured":"Carnein M, Assenmacher D, Trautmann H (2017a) An empirical comparison of stream clustering algorithms. In: Proceedings of the ACM international conference on computing frontiers (CF \u201917). ACM, pp 361\u2013365","DOI":"10.1145\/3075564.3078887"},{"key":"576_CR29","doi-asserted-by":"crossref","unstructured":"Carnein M, Assenmacher D, Trautmann H (2017b) Stream clustering of chat messages with applications to twitch streams. In Proceedings of the 36th international conference on conceptual modeling (ER\u201917). Springer International Publishing, pp 79\u201388","DOI":"10.1007\/978-3-319-70625-2_8"},{"key":"576_CR30","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, KDD \u201907, ACM, San Jose, pp 133\u2013142","DOI":"10.1145\/1281192.1281210"},{"key":"576_CR31","doi-asserted-by":"crossref","unstructured":"Dang XH, Lee V, Ng WK, Ciptadi A, Ong KL (2009a) An EM-based algorithm for clustering data streams in sliding windows. In: Zhou X, Yokota H, Deng K, Liu Q (eds) Proceedings of the 14th international conference on database systems for advanced applications (DASFAA 2009). Springer, Berlin, pp 230\u2013235","DOI":"10.1007\/978-3-642-00887-0_18"},{"key":"576_CR32","first-page":"660","volume-title":"Incremental and adaptive clustering stream data over sliding window","author":"XH Dang","year":"2009","unstructured":"Dang XH, Lee VCS, Ng WK, Ong KL (2009b) Incremental and adaptive clustering stream data over sliding window. Springer, Berlin, pp 660\u2013674"},{"key":"576_CR33","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining. AAAI Press, pp 226\u2013231"},{"issue":"1","key":"576_CR34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1145\/360402.360419","volume":"2","author":"F Farnstrom","year":"2000","unstructured":"Farnstrom F, Lewis J, Elkan C (2000) Scalability for clustering algorithms revisited. SIGKDD Explor Newsl 2(1):51\u201357","journal-title":"SIGKDD Explor Newsl"},{"key":"576_CR35","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: Algorithms - ESA 2013\u2014Proceedings of 21st annual European symposium, Sophia Antipolis, pp 481\u2013492. \n                    http:\/\/ls2-www.cs.tu-dortmund.de\/grav\/de\/bico\n                    \n                  . Accessed 27 Dec 2018","DOI":"10.1007\/978-3-642-40450-4_41"},{"issue":"2","key":"576_CR36","first-page":"139","volume":"2","author":"DH Fisher","year":"1987","unstructured":"Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2(2):139\u2013172","journal-title":"Mach Learn"},{"issue":"1","key":"576_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10618-011-0242-x","volume":"26","author":"A Forestiero","year":"2013","unstructured":"Forestiero A, Pizzuti C, Spezzano G (2013) A single pass algorithm for clustering evolving data streams based on swarm intelligence. Data Min Knowl Discov 26(1):1\u201326","journal-title":"Data Min Knowl Discov"},{"key":"576_CR38","first-page":"420","volume-title":"An incremental data stream clustering algorithm based on dense units detection","author":"J Gao","year":"2005","unstructured":"Gao J, Li J, Zhang Z, Tan P-N (2005) An incremental data stream clustering algorithm based on dense units detection. Springer, Berlin, pp 420\u2013425"},{"key":"576_CR39","doi-asserted-by":"crossref","unstructured":"Gao X, Ferrara E, Qiu J (2015) Parallel clustering of high-dimensional social media data streams. arXiv:1502.00316","DOI":"10.1109\/CCGrid.2015.19"},{"key":"576_CR40","doi-asserted-by":"crossref","unstructured":"Ghesmoune M, Azzag H, Lebbah M (2014) G-Stream: growing neural gas over data stream. In: Loo CK, Siah YK, Wong KW, Jin AT, Huang K (eds) Proceedings of neural information processing: 21st international conference, ICONIP 2014, Kuching, Malaysia, November 3\u20136, 2014, Part I. Springer International Publishing, pp 207\u2013214","DOI":"10.1007\/978-3-319-12637-1_26"},{"key":"576_CR41","first-page":"134","volume-title":"Clustering over data streams based on growing neural gas","author":"M Ghesmoune","year":"2015","unstructured":"Ghesmoune M, Lebbah M, Azzag H (2015) Clustering over data streams based on growing neural gas. Springer, Berlin, pp 134\u2013145"},{"issue":"1","key":"576_CR42","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s41044-016-0011-3","volume":"1","author":"M Ghesmoune","year":"2016","unstructured":"Ghesmoune M, Lebbah M, Azzag H (2016) State-of-the-art on clustering data streams. Big Data Anal 1(1):13","journal-title":"Big Data Anal"},{"issue":"3","key":"576_CR43","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TKDE.2003.1198387","volume":"15","author":"S Guha","year":"2003","unstructured":"Guha S, Meyerson A, Mishra N, Motwani R, O\u2019Callaghan L (2003) Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng 15(3):515\u2013528","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"576_CR44","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 Trans Knowl Data Eng 28(6):1449\u20131461","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"576_CR45","unstructured":"Hahsler M, Bolanos M, Forrest J (2015) streamMOA: interface for MOA stream clustering algorithms. \n                    https:\/\/cran.r-project.org\/web\/packages\/streamMOA\/\n                    \n                  . Accessed 27 Dec 2018"},{"key":"576_CR46","unstructured":"Hahsler M, Bolanos M, Forrest J, Carnein M, Assenmacher D (2018) stream: infrastructure for data stream mining. \n                    https:\/\/cran.r-project.org\/web\/packages\/stream\/\n                    \n                  . Accessed 27 Dec 2018"},{"key":"576_CR47","doi-asserted-by":"crossref","unstructured":"Hassani M, Kranen P, Seidl T (2011) Precise anytime clustering of noisy sensor data with logarithmic complexity. In: Proceedings of 5th international workshop on knowledge discovery from sensor data (SensorKDD 2011) in conjunction with 17th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2011), ACM, San Diego, pp 52\u201360","DOI":"10.1145\/2003653.2003659"},{"key":"576_CR48","first-page":"311","volume-title":"Density-based projected clustering of data streams","author":"M Hassani","year":"2012","unstructured":"Hassani M, Spaus P, Gaber MM, Seidl T (2012) Density-based projected clustering of data streams. Springer, Berlin, pp 311\u2013324"},{"key":"576_CR49","first-page":"446","volume-title":"Subspace MOA: subspace stream clustering evaluation using the MOA framework","author":"M Hassani","year":"2013","unstructured":"Hassani M, Kim Y, Seidl T (2013) Subspace MOA: subspace stream clustering evaluation using the MOA framework. Springer, Berlin, pp 446\u2013449"},{"key":"576_CR50","unstructured":"Hassani M, Hansen M, Kim Y, Seidl T (2016) subspaceMOA: interface to \u2019subspaceMOA\u2019. \n                    https:\/\/cran.r-project.org\/web\/packages\/subspaceMOA\/\n                    \n                  . Accessed 27 Dec 2018"},{"key":"576_CR51","unstructured":"Huawei Noah\u2019s Ark Lab (2015). streamDM. \n                    http:\/\/huawei-noah.github.io\/streamDM\/\n                    \n                  . Accessed 27 Dec 2018"},{"key":"576_CR52","unstructured":"Hutter F, Hoos HH, St\u00fctzle T (2007) Automatic algorithm configuration based on local search. In: Proceedings of the twenty-second conference on artifical intelligence (AAAI \u201907), pp 1152\u20131157"},{"key":"576_CR53","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1613\/jair.2861","volume":"36","author":"F Hutter","year":"2009","unstructured":"Hutter F, Hoos HH, Leyton-Brown K, St\u00fctzle T (2009) ParamILS: an automatic algorithm configuration framework. J Artif Intell Res 36:267\u2013306","journal-title":"J Artif Intell Res"},{"key":"576_CR54","doi-asserted-by":"crossref","unstructured":"Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: Proceedings of LION-5, pp 507\u2013523","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"576_CR55","first-page":"264","volume-title":"SOStream: self organizing density-based clustering over data stream","author":"C Isaksson","year":"2012","unstructured":"Isaksson C, Dunham MH, Hahsler M (2012) SOStream: self organizing density-based clustering over data stream. Springer, Berlin, pp 264\u2013278"},{"issue":"1","key":"576_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14257\/ijaiasd.2014.2.1.01","volume":"2","author":"N Ismael","year":"2014","unstructured":"Ismael N, Alzaalan M, Ashour W (2014) Improved multi threshold birch clustering algorithm 2(1):1\u201310. \n                    https:\/\/doi.org\/10.14257\/ijaiasd.2014.2.1.01","journal-title":"Improved multi threshold birch clustering algorithm"},{"key":"576_CR57","doi-asserted-by":"crossref","unstructured":"Jia C, Tan C, Yong A (2008) A grid and density-based clustering algorithm for processing data stream. In: Second international conference on genetic and evolutionary computing (WGEC \u201908), pp 517\u2013521","DOI":"10.1109\/WGEC.2008.32"},{"issue":"1","key":"576_CR58","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/BF00337288","volume":"43","author":"T Kohonen","year":"1982","unstructured":"Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59\u201369","journal-title":"Biol Cybern"},{"key":"576_CR59","first-page":"251","volume-title":"Continuous trend-based clustering in data streams","author":"M Kontaki","year":"2008","unstructured":"Kontaki M, Papadopoulos AN, Manolopoulos Y (2008) Continuous trend-based clustering in data streams. Springer, Berlin, pp 251\u2013262"},{"key":"576_CR60","doi-asserted-by":"crossref","unstructured":"Kranen P, Assent I, Baldauf C, Seidl T (2009) Self-adaptive anytime stream clustering. In: 9th IEEE international conference on data mining (ICDM \u201909), pp 249\u2013258","DOI":"10.1109\/ICDM.2009.47"},{"key":"576_CR61","doi-asserted-by":"crossref","unstructured":"Kranen P, Assent I, Baldauf C, Seidl T (2011a) The ClusTree: indexing micro-clusters for anytime stream mining. In: Knowledge and information systems journal (Springer KAIS), Vol 29, Issue 2. Springer, London, pp 249\u2013272","DOI":"10.1007\/s10115-010-0342-8"},{"key":"576_CR62","first-page":"405","volume-title":"Hierarchical clustering for real-time stream data with noise","author":"P Kranen","year":"2011","unstructured":"Kranen P, Reidl F, Villaamil FS, Seidl T (2011b) Hierarchical clustering for real-time stream data with noise. Springer, Berlin, pp 405\u2013413"},{"key":"576_CR63","doi-asserted-by":"crossref","unstructured":"Lin J, Lin H (2009) A density-based clustering over evolving heterogeneous data stream. In: 2009 ISECS international colloquium on computing, communication, control, and management, vol 4, pp 275\u2013277","DOI":"10.1109\/CCCM.2009.5267735"},{"key":"576_CR64","doi-asserted-by":"crossref","unstructured":"Liu LX, Huang H, Guo YF, Chen FC (2009) rDenStream, a clustering algorithm over an evolving data stream. In: 2009 international conference on information engineering and computer science, pp 1\u20134","DOI":"10.1109\/ICIECS.2009.5363379"},{"key":"576_CR65","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez M, Dubois-Lacoste J, C\u00e1ceres LP, St\u00fctzle T, Birattari M (2016) The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect 3:43\u201358","journal-title":"Oper Res Perspect"},{"key":"576_CR66","first-page":"169","volume-title":"A-BIRCH: automatic threshold estimation for the BIRCH clustering algorithm","author":"B Lorbeer","year":"2017","unstructured":"Lorbeer B, Kosareva A, Deva B, Softi\u0107 D, Ruppel P, K\u00fcpper A (2017) A-BIRCH: automatic threshold estimation for the BIRCH clustering algorithm. Springer, Berlin, pp 169\u2013178"},{"key":"576_CR67","first-page":"662","volume-title":"Connectivity based stream clustering using localised density exemplars","author":"S L\u00fchr","year":"2008","unstructured":"L\u00fchr S, Lazarescu M (2008) Connectivity based stream clustering using localised density exemplars. Springer, Berlin, pp 662\u2013672"},{"issue":"1","key":"576_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.datak.2008.08.006","volume":"68","author":"S L\u00fchr","year":"2009","unstructured":"L\u00fchr S, Lazarescu M (2009) Incremental clustering of dynamic data streams using connectivity based representative points. Data Knowl Eng 68(1):1\u201327","journal-title":"Data Knowl Eng"},{"key":"576_CR69","unstructured":"Ma WH (2014) Survey on data streams clustering techniques. In: Manufacture engineering, quality and production system III, volume 933 of Advanced Materials Research. Trans Tech Publications, pp 768\u2013773"},{"key":"576_CR70","unstructured":"Martinetz T, Schulten K et al (1991) A \u201cneural-gas\u201d network learns topologies. University of Illinois at Urbana-Champaign"},{"key":"576_CR71","doi-asserted-by":"crossref","unstructured":"Meesuksabai W, Kangkachit T, Waiyamai K (2011) Hue-Stream: evolution-based clustering technique for heterogeneous data streams with uncertainty. In: Tang J, King I, Chen L, Wang J (eds) ADMA, volume 7121 of Lecture Notes in Computer Science. Springer, pp 27\u201340","DOI":"10.1007\/978-3-642-25856-5_3"},{"key":"576_CR72","unstructured":"Motoyoshi M, Miura T, Shioya I (2004) Clustering stream data by regression analysis. In: Proceedings of the second workshop on Australasian information security, data mining and web intelligence, and software internationalisation, volume\u00a032 of ACSW Frontiers \u201904, Australian Computer Society, Darlinghurst, pp 115\u2013120"},{"key":"576_CR73","first-page":"1","volume":"7","author":"M Mousavi","year":"2015","unstructured":"Mousavi M, Bakar AA, Vakilian M (2015) Data stream clustering algorithms: a review. Int J Adv Soft Comput Appl 7:1\u201315","journal-title":"Int J Adv Soft Comput Appl"},{"issue":"3","key":"576_CR74","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","volume":"45","author":"H-L Nguyen","year":"2015","unstructured":"Nguyen H-L, Woon Y-K, Ng W-K (2015) A survey on data stream clustering and classification. Knowl Inf Syst 45(3):535\u2013569","journal-title":"Knowl Inf Syst"},{"key":"576_CR75","doi-asserted-by":"crossref","unstructured":"Ntoutsi I, Zimek A, Palpanas T, Kr\u00f6ger P, Kriegel H-P (2012) Density-based projected clustering over high dimensional data streams. In: Proceedings of the 2012 SIAM international conference on data mining, pp 987\u2013998","DOI":"10.1137\/1.9781611972825.85"},{"key":"576_CR76","doi-asserted-by":"crossref","unstructured":"O\u2019Callaghan L, Mishra N, Meyerson A, Guha S, Motwani R (2002) Streaming-data algorithms for high-quality clustering. In: Proceedings of the 18th international conference on data engineering (ICDE), pp 685\u2013694","DOI":"10.1109\/ICDE.2002.994785"},{"issue":"1","key":"576_CR77","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1145\/974121.974127","volume":"33","author":"NH Park","year":"2004","unstructured":"Park NH, Lee WS (2004) Statistical Grid-based clustering over data streams. SIGMOD Rec 33(1):32\u201337","journal-title":"SIGMOD Rec"},{"issue":"2","key":"576_CR78","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.datak.2007.04.003","volume":"63","author":"NH Park","year":"2007","unstructured":"Park NH, Lee WS (2007a) Cell trees: an adaptive synopsis structure for clustering multi-dimensional on-line data streams. Data Knowl Eng 63(2):528\u2013549","journal-title":"Data Knowl Eng"},{"key":"576_CR79","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1145\/1321440.1321551","volume-title":"Proceedings of the sixteenth ACM conference on conference on information and knowledge management","author":"NH Park","year":"2007","unstructured":"Park NH, Lee WS (2007b) Grid-based subspace clustering over data streams. In: Proceedings of the sixteenth ACM conference on conference on information and knowledge management, ACM, New York, pp 801\u2013810"},{"key":"576_CR80","doi-asserted-by":"crossref","unstructured":"Ren J, Ma R (2009) Density-based data streams clustering over sliding windows. In: 2009 Sixth international conference on fuzzy systems and knowledge discovery, volume\u00a05, pp 248\u2013252","DOI":"10.1109\/FSKD.2009.553"},{"issue":"1","key":"576_CR81","first-page":"83","volume":"6","author":"J Ren","year":"2011","unstructured":"Ren J, Cai B, Hu C (2011) Clustering over data streams based on grid density and index tree. J Converg Inf Technol 6(1):83\u201393","journal-title":"J Converg Inf Technol"},{"key":"576_CR82","first-page":"216","volume-title":"C-DBSCAN: density-based clustering with constraints","author":"C Ruiz","year":"2007","unstructured":"Ruiz C, Spiliopoulou M, Menasalvas E (2007) C-DBSCAN: density-based clustering with constraints. Springer, Berlin, pp 216\u2013223"},{"key":"576_CR83","first-page":"287","volume-title":"C-DenStream: using domain knowledge on a data stream","author":"C Ruiz","year":"2009","unstructured":"Ruiz C, Menasalvas E, Spiliopoulou M (2009) C-DenStream: using domain knowledge on a data stream. Springer, Berlin, pp 287\u2013301"},{"issue":"1","key":"576_CR84","doi-asserted-by":"publisher","first-page":"13:1","DOI":"10.1145\/2522968.2522981","volume":"46","author":"JA Silva","year":"2013","unstructured":"Silva JA, Faria ER, Barros RC, Hruschka ER, de Carvalho AC, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46(1):13:1\u201313:31","journal-title":"ACM Comput Surv"},{"key":"576_CR85","doi-asserted-by":"crossref","unstructured":"Spinosa EJ, de\u00a0Leon F\u00a0de Carvalho AP, Gama J (2007) Olindda: a cluster-based approach for detecting novelty and concept drift in data streams. In: Proceedings of the 2007 ACM symposium on applied computing. ACM, pp 448\u2013452","DOI":"10.1145\/1244002.1244107"},{"key":"576_CR86","doi-asserted-by":"crossref","unstructured":"Steil J, Huang MX, Bulling A (2018) Fixation detection for head-mounted eye tracking based on visual similarity of gaze targets. In: Proceedings of international symposium on eye tracking research and applications (ETRA), pp 23:1\u201323:9","DOI":"10.1145\/3204493.3204538"},{"key":"576_CR87","doi-asserted-by":"crossref","unstructured":"Tasoulis DK, Adams NM, Hand DJ (2006) Unsupervised clustering in streaming data. In: Sixth IEEE international conference on data mining\u2013workshops (ICDMW\u201906), pp 638\u2013642","DOI":"10.1109\/ICDMW.2006.165"},{"key":"576_CR88","unstructured":"Tasoulis D, Adams N, Weston DJ, Hand DJ (2008) Mining information from plastic card transaction streams. In: Proceedings in computational statistics: 18th symposium (COMPSTAT 2008), volume\u00a02, pp 315\u2013322"},{"issue":"6","key":"576_CR89","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1364\/JOSAA.7.001055","volume":"7","author":"J Theiler","year":"1990","unstructured":"Theiler J (1990) Estimating fractal dimension. J Opt Soc Am A 7(6):1055\u20131073","journal-title":"J Opt Soc Am A"},{"issue":"2","key":"576_CR90","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1111\/1467-9868.00293","volume":"63","author":"R Tibshirani","year":"2001","unstructured":"Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B (Stat Methodol) 63(2):411\u2013423","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"issue":"3","key":"576_CR91","doi-asserted-by":"publisher","first-page":"12:1","DOI":"10.1145\/1552303.1552305","volume":"3","author":"L Tu","year":"2009","unstructured":"Tu L, Chen Y (2009) Stream data clustering based on grid density and attraction. ACM Trans Knowl Discov Data 3(3):12:1\u201312:27","journal-title":"ACM Trans Knowl Discov Data"},{"key":"576_CR92","first-page":"605","volume-title":"E-Stream: evolution-based technique for stream clustering","author":"K Udommanetanakit","year":"2007","unstructured":"Udommanetanakit K, Rakthanmanon T, Waiyamai K (2007) E-Stream: evolution-based technique for stream clustering. Springer, Berlin, pp 605\u2013615"},{"key":"576_CR93","doi-asserted-by":"crossref","unstructured":"van Rijn JN, Holmes G, Pfahringer B, Vanschoren J (2014) Algorithm selection on data streams. In: D\u017eeroski S, Panov P, Kocev D, Todorovski L (eds) Proceedings of the 17th international conference on discovery science (DS), volume 8777 of lecture notes in computer science (LNCS). Springer, pp 325\u2013336","DOI":"10.1007\/978-3-319-11812-3_28"},{"issue":"1","key":"576_CR94","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10994-017-5686-9","volume":"107","author":"J Rijn van","year":"2018","unstructured":"van Rijn J, Nicolaas GH, Pfahringer B, Vanschoren J (2018) The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach Learn 107(1):149\u2013176","journal-title":"Mach Learn"},{"issue":"3","key":"576_CR95","doi-asserted-by":"publisher","first-page":"14:1","DOI":"10.1145\/1552303.1552307","volume":"3","author":"L Wan","year":"2009","unstructured":"Wan L, Ng WK, Dang XH, Yu PS, Zhang K (2009) Density-based clustering of data streams at multiple resolutions. ACM Trans Knowl Discov Data 3(3):14:1\u201314:28","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"6","key":"576_CR96","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1109\/TKDE.2011.263","volume":"25","author":"CD Wang","year":"2013","unstructured":"Wang CD, Lai JH, Huang D, Zheng WS (2013) SVStream: a support vector-based algorithm for clustering data streams. IEEE Trans Knowl Data Eng 25(6):1410\u20131424","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"576_CR97","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1145\/2858036.2858107","volume-title":"Proceedings of the 2016 CHI conference on human factors in computing systems","author":"G Wang","year":"2016","unstructured":"Wang G, Zhang X, Tang S, Zheng H, Zhao BY (2016) Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI conference on human factors in computing systems, ACM, New York, pp 225\u2013236"},{"key":"576_CR98","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-4651-1","volume-title":"Market segmentation","author":"M Wedel","year":"2000","unstructured":"Wedel M, Kamakura WA (2000) Market segmentation, 2nd edn. Springer, US","edition":"2"},{"key":"576_CR99","doi-asserted-by":"crossref","unstructured":"Yang C, Zhou J (2006) HClustream: a novel approach for clustering evolving heterogeneous data stream. In: Sixth IEEE international conference on data mining\u2014workshops (ICDMW\u201906), pp 682\u2013688","DOI":"10.1109\/ICDMW.2006.89"},{"key":"576_CR100","doi-asserted-by":"crossref","unstructured":"Yang Y, Liu Z, Zhang Jp, Yang J (2012) Dynamic density-based clustering algorithm over uncertain data streams. In: 2012 9th international conference on fuzzy systems and knowledge discovery, pp 2664\u20132670","DOI":"10.1109\/FSKD.2012.6233800"},{"key":"576_CR101","first-page":"334","volume-title":"Self-adaptive change detection in streaming data with non-stationary distribution","author":"X Zhang","year":"2010","unstructured":"Zhang X, Wang W (2010) Self-adaptive change detection in streaming data with non-stationary distribution. Springer, Berlin, pp 334\u2013345"},{"key":"576_CR102","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/233269.233324","volume-title":"Proceedings of the 1996 ACM SIGMOD international conference on management of data","author":"T Zhang","year":"1996","unstructured":"Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD international conference on management of data, ACM, Montreal, pp 103\u2013114"},{"issue":"2","key":"576_CR103","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1023\/A:1009783824328","volume":"1","author":"T Zhang","year":"1997","unstructured":"Zhang T, Ramakrishnan R, Livny M (1997) BIRCH: a new data clustering algorithm and its applications. Data Mini Knowl Discov 1(2):141\u2013182","journal-title":"Data Mini Knowl Discov"},{"key":"576_CR104","doi-asserted-by":"crossref","unstructured":"Zhang X, Germain C, Sebag M (2010) Adaptively detecting changes in autonomic grid computing. In: 2010 11th IEEE\/ACM international conference on grid computing, pp 387\u2013392","DOI":"10.1109\/GRID.2010.5698017"},{"issue":"2","key":"576_CR105","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s10115-007-0070-x","volume":"15","author":"A Zhou","year":"2007","unstructured":"Zhou A, Cao F, Qian W, Jin C (2007a) Tracking clusters in evolving data streams over sliding windows. Knowl Inf Syst 15(2):181\u2013214","journal-title":"Knowl Inf Syst"},{"key":"576_CR106","doi-asserted-by":"crossref","unstructured":"Zhou A, Cao F, Yan Y, Sha C, He X (2007b) Distributed data stream clustering: a fast EM-based approach. In: 2007 IEEE 23rd international conference on data engineering, pp 736\u2013745","DOI":"10.1109\/ICDE.2007.367919"},{"key":"576_CR107","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/B978-155860869-6\/50039-1","volume-title":"Proceedings of the 28th international conference on very large data bases","author":"Y Zhu","year":"2002","unstructured":"Zhu Y, Shasha D (2002) StatStream: statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th international conference on very large data bases, VLDB Endowment, Hong Kong, pp 358\u2013369"}],"container-title":["Business &amp; Information Systems Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12599-019-00576-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-019-00576-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-019-00576-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T19:16:17Z","timestamp":1579547777000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12599-019-00576-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,21]]},"references-count":107,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["576"],"URL":"https:\/\/doi.org\/10.1007\/s12599-019-00576-5","relation":{},"ISSN":["2363-7005","1867-0202"],"issn-type":[{"value":"2363-7005","type":"print"},{"value":"1867-0202","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,21]]},"assertion":[{"value":"20 June 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}