{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:31:07Z","timestamp":1771335067428,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2017,2,17]],"date-time":"2017-02-17T00:00:00Z","timestamp":1487289600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (CN)","doi-asserted-by":"publisher","award":["Nos. 61165009, 61663004, 61262005, 61363035, 61365009"],"award-info":[{"award-number":["Nos. 61165009, 61663004, 61262005, 61363035, 61365009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1007\/s13042-016-0628-6","type":"journal-article","created":{"date-parts":[[2017,2,17]],"date-time":"2017-02-17T12:35:00Z","timestamp":1487334900000},"page":"1085-1100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Combined constraint-based with metric-based in semi-supervised clustering ensemble"],"prefix":"10.1007","volume":"9","author":[{"given":"Siting","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Canlong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,2,17]]},"reference":[{"issue":"3","key":"628_CR1","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TKDE.2010.215","volume":"24","author":"L Wu","year":"2010","unstructured":"Wu L, Hoi S C H, Jin R, Zhu J, Yu N (2010) Learning bregman distance functions for semi-supervised clustering. IEEE Trans Knowl Data Eng 24(3):478\u2013491","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"628_CR2","first-page":"583","volume":"3","author":"A Strehl","year":"2003","unstructured":"Strehl A, Ghosh J, Cardie C (2003) Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583\u2013617","journal-title":"J Mach Learn Res"},{"key":"628_CR3","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/978-3-642-38562-9_26","volume":"7923","author":"L Du","year":"2013","unstructured":"Du L, Shen YD, Shen Z, Wang J, Xu Z (2013) A self-supervised framework for clustering ensemble. Lect Notes Comput Sci 7923:253\u2013264","journal-title":"Lect Notes Comput Sci"},{"issue":"2","key":"628_CR4","first-page":"185","volume":"18","author":"ZF Hao","year":"2015","unstructured":"Hao ZF, Wang LJ, Cai RC, Wen W (2015) An improved clustering ensemble method based link analysis. World Wide Web-internet & Web. Inform Syst 18(2):185\u2013195","journal-title":"Inform Syst"},{"issue":"4","key":"628_CR5","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/TCBB.2014.2315996","volume":"11","author":"Z Yu","year":"2014","unstructured":"Yu Z, Chen H, You J, Wong HS, Liu J, Li L et al (2014) Double selection based semi-supervised clustering ensemble for tumor clustering from gene expression profiles. IEEE\/ACM Trans Comput Biol Bioinform 11(4):727\u2013740","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"3","key":"628_CR6","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TKDE.2015.2499200","volume":"28","author":"Z Yu","year":"2016","unstructured":"Yu Z, Luo P, You J, Wong HS, Leung H, Wu S et al (2016) Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Trans Knowl Data Eng 28(3):701\u2013714","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"628_CR7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TKDE.2013.22","volume":"26","author":"S Xiong","year":"2014","unstructured":"Xiong S, Azimi J, Fern XZ (2014) Active learning of constraints for semi-supervised clustering. IEEE Trans Knowl Data Eng 26(1):43\u201354","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"628_CR8","first-page":"1","volume":"46","author":"D Wang","year":"2015","unstructured":"Wang D, Gao X, Wang X (2015) Semi-supervised nonnegative matrix factorization via constraint propagation. IEEE Trans Cybern 46:1\u201312.","journal-title":"IEEE Trans Cybern"},{"key":"628_CR9","first-page":"1","volume":"24","author":"Y Yan","year":"2012","unstructured":"Yan Y, Chen L, Nguyen D T (2012) Semi-supervised clustering with multi-viewpoint based similarity measure. IEEE Int Jt Conf Neural Netw (IJCNN), 24, 1\u20138.","journal-title":"IEEE Int Jt Conf Neural Netw (IJCNN)"},{"issue":"15","key":"628_CR10","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.knosys.2012.05.016","volume":"35","author":"X Yin","year":"2012","unstructured":"Yin X, Shu T, Huang Q (2012) Semi-supervised fuzzy clustering with metric learning and entropy regularization. Knowl-Based Syst 35(15):304\u2013311","journal-title":"Knowl-Based Syst"},{"key":"628_CR11","doi-asserted-by":"crossref","unstructured":"Bilenko M, Basu S, Mooney RJ (2004) Integrating constraints and metric learning in semi-supervised clustering. The 21st International Conference on Machine Learning, 81\u201388.","DOI":"10.1145\/1015330.1015360"},{"issue":"4","key":"628_CR12","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1016\/j.patcog.2009.11.005","volume":"43","author":"X Yin","year":"2010","unstructured":"Yin X, Chen S, Hu E, Zhang D (2010) Semi-supervised clustering with metric learning: an adaptive kernel method. Pattern Recognit 43(4):1320\u20131333","journal-title":"Pattern Recognit"},{"key":"628_CR13","unstructured":"Lin L, Qu W, Yu X (2009) A semi-supervised clustering algorithm based on rough reduction. International Conference on Chinese Control and Decision Conference, 5427\u20135431."},{"issue":"6","key":"628_CR14","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.knosys.2009.06.009","volume":"22","author":"H Zhang","year":"2009","unstructured":"Zhang H, Lu J (2009) Semi-supervised fuzzy clustering: a kernel-based approach. Knowl-Based Syst 22(6):477\u2013481","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"628_CR15","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1109\/TPAMI.2014.2359454","volume":"37","author":"N Arzeno","year":"2015","unstructured":"Arzeno N, Vikalo H (2015) Semi-supervised affinity propagation with soft instance-level constraints. IEEE Trans Pattern Anal Mach Intell 37(5):1041\u20131052","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"628_CR16","unstructured":"Basu S, Banerjee A, Mooney RJ (2002) Semi-supervised clustering by seeding. In: Proceedings of the nineteenth international conference on machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 27\u201334"},{"key":"628_CR17","doi-asserted-by":"crossref","unstructured":"Pelleg D, Baras D (2007) K-means with large and noisy constraint sets. In: Kok JN, Koronacki J, Mantaras RL, Matwin S, Mladeni\u010d D, Skowron A (eds) Machine learning: ECML 2007. Lecture notes in computer science, vol 4701. Springer, Berlin, Heidelberg, pp 674\u2013682","DOI":"10.1007\/978-3-540-74958-5_67"},{"issue":"5","key":"628_CR18","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1016\/j.patcog.2007.10.004","volume":"41","author":"N Grira","year":"2008","unstructured":"Grira N, Crucianu M, Boujemaa N (2008) Active semi-supervised fuzzy clustering. Pattern Recognit 41(5):1834\u20131844","journal-title":"Pattern Recognit"},{"key":"628_CR19","doi-asserted-by":"crossref","unstructured":"Zeng H, Cheung Y M, Member S (2012) Semi-supervised maximum margin clustering with pairwise constraints. IEEE Trans Knowl Data Eng 24(5):926\u2013939","DOI":"10.1109\/TKDE.2011.68"},{"issue":"1","key":"628_CR20","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00521-012-1207-8","volume":"24","author":"S Ding","year":"2014","unstructured":"Ding S, Jia H, Zhang L, Jin F (2014) Research of semi-supervised spectral clustering algorithm based on pairwise constraints. Neural Comput Appl 24(1), 211\u2013219.","journal-title":"Neural Comput Appl"},{"key":"628_CR21","doi-asserted-by":"crossref","unstructured":"Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: Proceedings of the 24th international conference on Machine learning. ACM, New York, pp 209\u2013216","DOI":"10.1145\/1273496.1273523"},{"issue":"1","key":"628_CR22","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger KQ, Blitzer J, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(1):207\u2013244","journal-title":"J Mach Learn Res"},{"key":"628_CR23","unstructured":"Huang M, Chen Y, Liu J, Ji W (2014) A large margin nearest cluster metric based semi-supervised clustering algorithm for brain fibers. International Conference on Game Theory for Networks, 1\u20135."},{"key":"628_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen N, Caruana R (2007) Consensus clusterings. In: Proceedings of the 7th IEEE international conference on data mining. IEEE Computer Society, Washington, DC, pp 607\u2013612","DOI":"10.1109\/ICDM.2007.73"},{"key":"628_CR25","unstructured":"Wang X, Han D, Han C (2013) Rough set based cluster ensemble selection. In: Proceedings of 16th International Conference on Information Fusion (FUSION). IEEE, Istanbul, Turkey, pp 438\u2013444"},{"key":"628_CR26","doi-asserted-by":"crossref","unstructured":"Wang H, Qi J, Zheng W, Wang M (2010) Semi-supervised cluster ensemble based on binary similarity matrix. IEEE International Conference on Information Management and Engineering, 251\u2013254.","DOI":"10.1109\/ICIME.2010.5478054"},{"key":"628_CR27","doi-asserted-by":"crossref","unstructured":"Chen D, Yang Y, Wang H, Mahmood A (2013) Convergence analysis of semi-supervised clustering ensemble. International Conference on Information Science and Technology (ICIST), 783\u2013788.","DOI":"10.1109\/ICIST.2013.6747660"},{"key":"628_CR28","doi-asserted-by":"crossref","unstructured":"Zhang D, Tan K, Chen S (2004) Semi-supervised kernel-based fuzzy c-means. In: Lecture notes computer science, vol 3316, pp 1229\u20131234","DOI":"10.1007\/978-3-540-30499-9_191"},{"issue":"2","key":"628_CR29","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TAC.1976.1101194","volume":"21","author":"DP Bertsekas","year":"1976","unstructured":"Bertsekas DP (1976) On the goldstein-levitin-polyak gradient projection method. IEEE Trans Autom Control 21(2):174\u2013184","journal-title":"IEEE Trans Autom Control"},{"key":"628_CR30","first-page":"159","volume":"2","author":"Y Na","year":"2013","unstructured":"Na Y, Yu J (2013) A pixel similarity method for spectral clustering image segmentation. J Nanjing Univ Nat Sci 2:159\u2013168","journal-title":"J Nanjing Univ Nat Sci"},{"key":"628_CR31","doi-asserted-by":"crossref","unstructured":"Fowlkes C, Martin D, Malik J (2003) Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches. In: IEEE conference on computer vision and pattern recognition, vol 2, pp 54\u201361","DOI":"10.1109\/CVPR.2003.1211452"},{"key":"628_CR32","first-page":"1124","volume":"2","author":"T Cour","year":"2005","unstructured":"Cour T, B\u00e9n\u00e9zit F, Shi J (2005) Spectral segmentation with multiscale graph decomposition. IEEE Comput Soc Conf Comput Vis Pattern Recog 2:1124\u20131131","journal-title":"IEEE Comput Soc Conf Comput Vis Pattern Recog"},{"issue":"5","key":"628_CR33","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","volume":"26","author":"D Martin","year":"2004","unstructured":"Martin D, Fowlkes C, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530\u2013549","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"628_CR34","first-page":"1855","volume":"4","author":"T Sun","year":"2011","unstructured":"Sun T, Ren Z, Ding S (2011) Region-based semi-supervised clustering image segmentation. Int Conf Nat Comput 4:1855\u20131858.","journal-title":"Int Conf Nat Comput"},{"key":"628_CR35","unstructured":"Lichman M (2013) UCI Machine Learning Repository. University of California, Irvine, CA School of Information and Computer Science. doi:http:\/\/archive.ics.uci.edu\/ml."},{"key":"628_CR36","first-page":"1214","volume":"2","author":"L Kuncheva","year":"2004","unstructured":"Kuncheva L, Hadjitodorov S B (2004) Using diversity in cluster ensembles. IEEE Int Conf Syst Man Cybern 2:1214\u20131219.","journal-title":"IEEE Int Conf Syst Man Cybern"},{"issue":"5","key":"628_CR37","first-page":"898","volume":"33","author":"P Arbel\u00e1ez","year":"2011","unstructured":"Arbel\u00e1ez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Software Eng 33(5):898\u2013916","journal-title":"IEEE Trans Software Eng"},{"issue":"12","key":"628_CR38","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/TKDE.2009.40","volume":"21","author":"F Wang","year":"2009","unstructured":"Wang F, Zhang C, Li T (2009) Clustering with local and global regularization. IEEE Trans Knowl Data Eng 21(12):1665\u20131678","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-016-0628-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-016-0628-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-016-0628-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,6,21]],"date-time":"2018-06-21T03:59:17Z","timestamp":1529553557000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-016-0628-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,17]]},"references-count":38,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2018,7]]}},"alternative-id":["628"],"URL":"https:\/\/doi.org\/10.1007\/s13042-016-0628-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,17]]}}}