{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T08:39:32Z","timestamp":1774773572255,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,1,7]],"date-time":"2016-01-07T00:00:00Z","timestamp":1452124800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Journal of Big Data"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1186\/s40537-015-0036-x","type":"journal-article","created":{"date-parts":[[2016,1,7]],"date-time":"2016-01-07T01:24:50Z","timestamp":1452129890000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Data stream clustering by divide and conquer approach based on vector model"],"prefix":"10.1186","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5479-7033","authenticated-orcid":false,"given":"Madjid","family":"Khalilian","sequence":"first","affiliation":[]},{"given":"Norwati","family":"Mustapha","sequence":"additional","affiliation":[]},{"given":"Nasir","family":"Sulaiman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,1,7]]},"reference":[{"issue":"3","key":"36_CR1","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","volume":"16","author":"R Xu","year":"2005","unstructured":"Xu R, Wunsch D. Survey of clustering algorithms. IEEE Trans Neural Netw. 2005;16(3):645\u201378.","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"36_CR2","first-page":"12","volume":"3","author":"L Tu","year":"2009","unstructured":"Tu L, Chen Y. Stream data clustering based on grid density and attraction. ACM Trans Knowl Discov Data (TKDD). 2009;3(3):12.","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Aggarwal CC. A Framework for Clustering Massive-Domain Data Streams, presented at ICDE \u201809. IEEE 25th International Conference on Data Engineering; 2009.","DOI":"10.1109\/ICDE.2009.13"},{"key":"36_CR4","doi-asserted-by":"crossref","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. Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng. 2003;15:515\u201328.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Yu PS. A framework for clustering massive text and categorical data streams. In: SDM; 2006.","DOI":"10.1137\/1.9781611972764.44"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, Ramakrishnan, Livny. BIRCH: an efficient data clustering method for very large databases. Presented at ACM SIGMOD Conference on Management of Data; 1996.","DOI":"10.1145\/233269.233324"},{"key":"36_CR7","unstructured":"Yunyue Z, Dennis S. StatStream: statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th international conference on Very Large Data Bases. Hong Kong, China: VLDB Endowment; 2002."},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Aggarwal C, Jiawei H, Jianyong W, Philip SY. A framework for clustering evolving data streams. In: Proceedings of the 29th international conference on Very large data bases\u2014Volume 29. Berlin, Germany: VLDB Endowment; 2003.","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Chen Y, Li T. Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2007.","DOI":"10.1145\/1281192.1281210"},{"key":"36_CR10","unstructured":"Cormode, G, Muthukrishnan, S, Wei Z. Conquering the Divide: Continuous Clustering of Distributed Data Streams. In: IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007, p. 1036\u201345."},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Rodrigues PP, Gama J, Pedroso JP. Hierarchical clustering of time-series data streams. In: IEEE transactions on knowledge and data engineering; 2007, p. 615\u201327.","DOI":"10.1109\/TKDE.2007.190727"},{"key":"36_CR12","unstructured":"Aoying Z, Feng C, Ying Y, Chaofeng S, Xiaofeng H. Distributed data stream clustering: a fast EM-based approach. Presented at IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007."},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Aggarwal CC. On high dimensional projected clustering of uncertain data streams. Presented at IEEE 25th International Conference on Data Engineering, 2009. ICDE \u201809.","DOI":"10.1109\/ICDE.2009.188"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Chen Z, He R, Li Y. Online fractal dimensionality reduction in time decaying stream environment. In: Eighth international conference on fuzzy systems and knowledge discovery (FSKD), vol 3. IEEE; 2011. p. 1480\u20134.","DOI":"10.1109\/FSKD.2011.6019844"},{"issue":"5","key":"36_CR15","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.patrec.2011.11.022","volume":"33","author":"Q Tu","year":"2012","unstructured":"Tu Q, Lu JF, Yuan B, Tang JB, Yang JY. Density-based hierarchical clustering for streaming data. Pattern Recognit Lett. 2012;33(5):641\u20135.","journal-title":"Pattern Recognit Lett"},{"key":"36_CR16","unstructured":"Guha S, Meyerson A, Mishra N, Motwani R, O\u2019Callaghan L. Streaming-data algorithms for high-quality clustering. Presented at Proceedings 18th International Conference on Data Engineering; 2002."},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Aggarwal CC. On High Dimensional Projected Clustering of Uncertain Data Streams. Presented at IEEE 25th International Conference on Data Engineering, ICDE \u201809.","DOI":"10.1109\/ICDE.2009.188"},{"key":"36_CR18","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10462-011-9209-y","volume":"36","author":"P Vivekanandan","year":"2011","unstructured":"Vivekanandan P, Nedunchezhian R. Mining data streams with concept drifts using genetic algorithm. Artif Intell Rev. 2011;36:163\u201378.","journal-title":"Artif Intell Rev"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Pardeshi B, Toshniwal D, Meghanathan N, Kaushik BK, Nagamalai D. Hierarchical clustering of projected data streams using cluster validity index advances in computer science and information technology. vol. 131, Communications in Computer and Information Science, Berlin: Springer; 2011. p. 551\u20139.","DOI":"10.1007\/978-3-642-17857-3_54"},{"key":"36_CR20","unstructured":"Cardoso DD, Lima PM, De Gregorio M, Gama J, Fran\u00e7a FM. Clustering data streams with weightless neural networks. In: ESANN; 2011."},{"key":"36_CR21","unstructured":"Ikonomovska E, Loskovska S, Gjorgjevik D. A survey of stream data mining. In: Proceedings of the 8th National Conference with International Participation. 2007. pp. 19\u201325."},{"issue":"2","key":"36_CR22","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1083784.1083789","volume":"34","author":"MM Gaber","year":"2005","unstructured":"Gaber MM, Zaslavsky A, Krishnaswamy S. Mining data streams: a review. ACM Sigmod Record. 2005;34(2):18\u201326.","journal-title":"ACM Sigmod Record"},{"key":"36_CR23","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/507515.507519","volume":"3","author":"B Daniel","year":"2002","unstructured":"Daniel B. Requirements for clustering data streams. SIGKDD Explor Newsl. 2002;3:23\u20137.","journal-title":"SIGKDD Explor Newsl"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Wang H, Fan W, Yu PS, Han J. Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM; 2003. pp. 226\u201335.","DOI":"10.1145\/956750.956778"},{"key":"36_CR25","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/507515.507519","volume":"3","author":"D Barbara","year":"2002","unstructured":"Barbara D. Requirements for clustering data streams. ACM SIGKDD Explorat Newslett. 2002;3:23\u20137.","journal-title":"ACM SIGKDD Explorat Newslett"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Aggarwal CC. A Framework for Clustering Massive-Domain Data Streams. Presented at IEEE 25th International Conference on Data Engineering, ICDE \u201809.","DOI":"10.1109\/ICDE.2009.13"},{"key":"36_CR27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10115-009-0241-z","volume":"24","author":"C Aggarwal","year":"2009","unstructured":"Aggarwal C, Yu P. On clustering massive text and categorical data streams. Knowl Inform Syst. 2009;24:171\u201396.","journal-title":"Knowl Inform Syst."},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Aggarwal, CC, Yu PS. A Framework for Clustering Uncertain Data Streams. Presented at IEEE 24th International Conference on Data Engineering, ICDE 2008.","DOI":"10.1109\/ICDE.2008.4497423"},{"key":"36_CR29","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/235968.233324","volume":"25","author":"Z Tian","year":"1996","unstructured":"Tian Z, Raghu R, Miron L. BIRCH: an efficient data clustering method for very large databases. SIGMOD Rec. 1996;25:103\u201314.","journal-title":"SIGMOD Rec."},{"key":"36_CR30","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TKDE.2008.21","volume":"20","author":"C Heinz","year":"2008","unstructured":"Heinz C, Seeger B. Cluster kernels: resource-aware kernel density estimators over streaming data. IEEE Trans Knowl Data Eng. 2008;20:880\u201393.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"36_CR31","first-page":"14","volume":"3","author":"L Wan","year":"2009","unstructured":"Wan L, Ng WK, Dang XH, Yu PS, Zhang K. Density-based clustering of data streams at multiple resolutions. ACM Trans Knowl Discov Data (TKDD). 2009;3(3):14","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"36_CR32","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.datak.2008.06.006","volume":"67","author":"MH Chehreghani","year":"2008","unstructured":"Chehreghani MH, Abolhassani H, Chehreghani MH. Improving density-based methods for hierarchical clustering of web pages. Data Knowl Eng. 2008;67:30\u201350.","journal-title":"Data Knowl Eng"},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Yang D, Rundensteiner EA, Ward MO. 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; 2009. p. 529\u201340.","DOI":"10.1145\/1516360.1516422"},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Lin G, Chen L. A grid and fractal dimension-based data stream clustering algorithm. In: International symposium on information science and engineering, ISISE'08, vol 1. IEEE; 2008. p. 66\u201370","DOI":"10.1109\/ISISE.2008.141"},{"key":"36_CR35","unstructured":"Wei J, Brice P. Data stream clustering and modeling using context-trees. Presented at 6th International Conference on Service Systems and Service Management, ICSSSM \u201809."},{"key":"36_CR36","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1007\/s00778-009-0134-5","volume":"18","author":"K Chen","year":"2009","unstructured":"Chen K, Liu L. HE-Tree: a framework for detecting changes in clustering structure for categorical data streams. VLDB J. 2009;18:1241\u201360.","journal-title":"VLDB J"},{"key":"36_CR37","unstructured":"Hongbin G, Ruiguang L, Jie H. A Kind of Data Stream Clustering Algorithm Based on Grid and Relative Density. Presented at Spring Congress on Engineering and Technology (S-CET); 2012."},{"key":"36_CR38","doi-asserted-by":"crossref","DOI":"10.1533\/9780857099440","volume-title":"Machin learning and data mining","author":"I Kononenko","year":"2007","unstructured":"Kononenko I, Kukar M. Machin learning and data mining. Chichester: Horwood Publishing; 2007."},{"key":"36_CR39","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1109\/TKDE.2013.146","volume":"26","author":"Z Xiangliang","year":"2014","unstructured":"Xiangliang Z, Furtlehner C, Germain-Renaud C, Sebag M. Data stream clustering with affinity propagation. Knowl Data Eng IEEE Trans o. 2014;26:1644\u201356.","journal-title":"Knowl Data Eng IEEE Trans o"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-015-0036-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40537-015-0036-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-015-0036-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T02:11:41Z","timestamp":1748743901000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.journalofbigdata.com\/content\/3\/1\/1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,7]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["36"],"URL":"https:\/\/doi.org\/10.1186\/s40537-015-0036-x","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,7]]},"article-number":"1"}}