{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:33:49Z","timestamp":1781267629147,"version":"3.54.1"},"reference-count":23,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2019,4,29]]},"DOI":"10.3233\/ida-183869","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:53:37Z","timestamp":1557831217000},"page":"717-732","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid data stream clustering by controlling decision error"],"prefix":"10.1177","volume":"23","author":[{"given":"Jeonghwa","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taek-Ho","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Hyuck","family":"Jun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-183869_ref1","doi-asserted-by":"crossref","unstructured":"C.C. Aggarwal, J. Han, J. Wang and P.S. Yu, A framework for clustering evolving data streams, in: Proceedings of the 29th International Conference on Very Large Data Bases (VLDB 2003), Vol. 29, 2003, pp. 81\u201392.","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"issue":"1","key":"10.3233\/IDA-183869_ref2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s11390-014-1416-y","article-title":"On density-based data streams clustering algorithms: A survey","volume":"29","author":"Amini","year":"2014","journal-title":"Journal of Computer Science and Technology"},{"issue":"3","key":"10.3233\/IDA-183869_ref3","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0168-1699(99)00046-0","article-title":"Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables","volume":"24","author":"Blackard","year":"1999","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/IDA-183869_ref5","doi-asserted-by":"crossref","unstructured":"F. Cao, M. Ester, W. Qian and A. Zhou, Density-based clustering over an evolving data stream with noise, in: Proc. SIAM Conf. Data Mining, 2006, pp. 326\u2013337.","DOI":"10.1137\/1.9781611972764.29"},{"key":"10.3233\/IDA-183869_ref6","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.ins.2016.01.071","article-title":"A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data","volume":"345","author":"Chen","year":"2016","journal-title":"Information Sciences"},{"issue":"2","key":"10.3233\/IDA-183869_ref8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0169-7439(01)00111-3","article-title":"Looking for natural patterns in data: Part 1. density-based approach","volume":"56","author":"Daszykowski","year":"2001","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"issue":"1","key":"10.3233\/IDA-183869_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"Journal of the Royal Statistical Society. Series B"},{"issue":"1","key":"10.3233\/IDA-183869_ref10","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s12559-015-9342-z","article-title":"An adaptive density data stream clustering","volume":"8","author":"Ding","year":"2016","journal-title":"Cognitive Computation"},{"key":"10.3233\/IDA-183869_ref11","unstructured":"M. Ester, H.-P. Kriegel, J. Sander and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), AAAI, 1996, pp. 226\u2013231."},{"key":"10.3233\/IDA-183869_ref12","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.snb.2015.03.028","article-title":"Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring","volume":"215","author":"Fonollosa","year":"2015","journal-title":"Sensors and Actuators B: Chemical"},{"key":"10.3233\/IDA-183869_ref13","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1093\/bioinformatics\/bti517","article-title":"Computational cluster validation in post-genomic data analysis","volume":"21","author":"Handl","year":"2005","journal-title":"Bioinformatics"},{"issue":"1","key":"10.3233\/IDA-183869_ref15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"Journal of Classification"},{"issue":"2","key":"10.3233\/IDA-183869_ref17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10115-010-0342-8","article-title":"The ClusTree: indexing micro-clusters for anytime stream mining","volume":"29","author":"Kranen","year":"2011","journal-title":"Knowledge and Information Systems"},{"key":"10.3233\/IDA-183869_ref18","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.knosys.2012.08.013","article-title":"PCA-based high-dimensional noisy data clustering via control of decision errors","volume":"37","author":"Lee","year":"2013","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"10.3233\/IDA-183869_ref20","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","article-title":"A survey on data stream clustering and classification","volume":"45","author":"Nguyen","year":"2015","journal-title":"Knowledge and Information Systems"},{"issue":"1","key":"10.3233\/IDA-183869_ref21","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10479-012-1238-7","article-title":"Clustering noise-included data by controlling decision errors","volume":"216","author":"Park","year":"2014","journal-title":"Annals of Operations Research"},{"issue":"336","key":"10.3233\/IDA-183869_ref22","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","article-title":"Objective criteria for the evaluation of clustering methods","volume":"66","author":"Rand","year":"1971","journal-title":"Journal of the American Statistical Association"},{"issue":"2","key":"10.3233\/IDA-183869_ref23","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","article-title":"Estimating the dimension of a model","volume":"6","author":"Schwarz","year":"1978","journal-title":"The Annals of Statistics"},{"key":"10.3233\/IDA-183869_ref24","doi-asserted-by":"crossref","unstructured":"M. Song and H. Wang, Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering, in: Proceedings of SPIE Conference on Intelligent Computing: Theory and Applications III, 2005.","DOI":"10.1117\/12.601724"},{"key":"10.3233\/IDA-183869_ref25","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1214\/aos\/1074290335","article-title":"The positive false discovery rate: a Bayesian interpretation and the q-value","volume":"31","author":"Storey","year":"2003","journal-title":"The Annals of Statistics"},{"issue":"3","key":"10.3233\/IDA-183869_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1552303.1552305","article-title":"Stream data clustering based on grid density and attraction","volume":"3","author":"Tu","year":"2009","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"9","key":"10.3233\/IDA-183869_ref27","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1162\/089976600300015088","article-title":"SMEM algorithm for mixture models","volume":"12","author":"Ueda","year":"2000","journal-title":"Neural Computation"},{"key":"10.3233\/IDA-183869_ref28","unstructured":"W. Wang, J. Yang and R. Muntz, Sting: A statistical information grid approach to spatial data mining, in: Proceedings of the International Conference on Very Large Data Bases, 1997, pp. 186\u2013195."}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-183869","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:18:22Z","timestamp":1777454302000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-183869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,29]]},"references-count":23,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/ida-183869","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,29]]}}}