{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T06:29:24Z","timestamp":1776407364505,"version":"3.51.2"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62176106"],"award-info":[{"award-number":["62176106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Key Project of NSFC","award":["U1836220"],"award-info":[{"award-number":["U1836220"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Classif"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00357-024-09489-9","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T06:02:35Z","timestamp":1723528955000},"page":"181-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Combining Semi-supervised Clustering and Classification Under a Generalized Framework"],"prefix":"10.1007","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6025-0333","authenticated-orcid":false,"given":"Zhen","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Lingyun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"9489_CR1","doi-asserted-by":"publisher","unstructured":"Basu, S., Banerjee, A., Mooney, A. &\u00a0Raymond, J. (2002). Semi-supervised clustering by seeding. In\u00a0Proceedings of the nineteenth international conference on machine learning (pp.\u00a027\u201334).\u00a0Morgan Kaufmann Publishers Inc.\u00a0https:\/\/doi.org\/10.5555\/645531.656012","DOI":"10.5555\/645531.656012"},{"key":"9489_CR2","doi-asserted-by":"publisher","unstructured":"Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In\u00a0Proceedings of the eleventh annual conference on computational learning theory (pp. 92\u2013100).\u00a0Association for Computing Machinery.\u00a0https:\/\/doi.org\/10.1145\/279943.279962","DOI":"10.1145\/279943.279962"},{"key":"9489_CR3","doi-asserted-by":"crossref","unstructured":"Chen, M., Du, Y., Zhang, Y., Qian, S., & Wang, C. (2022). Semi-supervised learning with multi-head co-training. In\u00a0Proceedings of the AAAI conference on artificial intelligence (Vol. 36(6), pp. 6278\u20136286).","DOI":"10.1609\/aaai.v36i6.20577"},{"issue":"4","key":"9489_CR4","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1007\/s11749-019-00690-2","volume":"29","author":"A Cholaquidis","year":"2020","unstructured":"Cholaquidis, A., Fraiman, R., & Sued, M. (2020). On Semi-Supervised Learning. TEST, 29(4), 914\u2013937.","journal-title":"TEST"},{"key":"9489_CR5","doi-asserted-by":"crossref","unstructured":"Dong-DongChen, W., & WeiGao, Z. (2018). Tri-net for semi-supervised deep learning. In\u00a0Proceedings of twenty-seventh international joint conference on artificial intelligence (pp.\u00a02014\u20132020).","DOI":"10.24963\/ijcai.2018\/278"},{"key":"9489_CR6","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.patcog.2017.09.038","volume":"74","author":"A-J Gallego","year":"2018","unstructured":"Gallego, A.-J., Calvo-Zaragoza, J., Valero-Mas, J. J., & Rico-Juan, J. R. (2018). Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation. Pattern Recognition, 74, 531\u2013543.","journal-title":"Pattern Recognition"},{"key":"9489_CR7","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.neucom.2012.08.020","volume":"101","author":"H Gan","year":"2013","unstructured":"Gan, H., Sang, N., Huang, R., Tong, X., & Dan, Z. (2013). Using clustering analysis to improve semi-supervised classification. Neurocomputing, 101, 290\u2013298.","journal-title":"Neurocomputing"},{"key":"9489_CR8","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.ins.2018.04.080","volume":"454","author":"H Gan","year":"2018","unstructured":"Gan, H., Huang, R., Luo, Z., Xi, X., & Gao, Y. (2018). On using supervised clustering analysis to improve classification performance. Information Sciences, 454, 216\u2013228.","journal-title":"Information Sciences"},{"key":"9489_CR9","doi-asserted-by":"publisher","unstructured":"Gertrudes, J. C., Zimek, A., Sander, J., & Campello, R. J. G. B. (2018). A unified framework of density-based clustering for semi-supervised classification. In\u00a0Proceedings of the 30th international conference on scientific and statistical database management. Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3221269.3223037","DOI":"10.1145\/3221269.3223037"},{"key":"9489_CR10","unstructured":"Goldman, S., & Zhou, Y. (2000). Enhancing supervised learning with unlabeled data. In\u00a0Proceedings of the seventeenth international conference on machine learning (pp. 327\u2013334)."},{"issue":"11","key":"9489_CR11","doi-asserted-by":"publisher","first-page":"9234","DOI":"10.1109\/TNNLS.2022.3157688","volume":"34","author":"M Gong","year":"2022","unstructured":"Gong, M., Zhou, H., Qin, A. K., Liu, W., & Zhao, Z. (2022). Self-paced co-training of graph neural networks for semi-supervised node classification. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 9234\u20139247.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"9489_CR12","unstructured":"Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., \u2026 Sugiyama, M. (2018). Co-teaching: Robust training of deep neural networks with extremely noisy labels. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 31). Curran Associates, Inc."},{"key":"9489_CR13","doi-asserted-by":"publisher","first-page":"109255","DOI":"10.1016\/j.patcog.2022.109255","volume":"136","author":"Q Huang","year":"2023","unstructured":"Huang, Q., Gao, R., & Akhavan, H. (2023). An ensemble hierarchical clustering algorithm based on merits at cluster and partition levels. Pattern Recognition, 136, 109255.","journal-title":"Pattern Recognition"},{"key":"9489_CR14","doi-asserted-by":"publisher","first-page":"109388","DOI":"10.1016\/j.patcog.2023.109388","volume":"138","author":"H Jia","year":"2023","unstructured":"Jia, H., Zhu, D., Huang, L., Mao, Q., Wang, L., & Song, H. (2023). Global and local structure preserving nonnegative subspace clustering. Pattern Recognition, 138, 109388.","journal-title":"Pattern Recognition"},{"key":"9489_CR15","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.knosys.2012.07.020","volume":"37","author":"Z Jiang","year":"2013","unstructured":"Jiang, Z., Zhang, S., & Zeng, J. (2013). A hybrid generative\/discriminative method for semi-supervised classification. Knowledge-Based Systems, 37, 137\u2013145.","journal-title":"Knowledge-Based Systems"},{"issue":"5","key":"9489_CR16","doi-asserted-by":"publisher","first-page":"5244","DOI":"10.1109\/TKDE.2022.3145347","volume":"35","author":"Z Jiang","year":"2022","unstructured":"Jiang, Z., Zhan, Y., Mao, Q., & Du, Y. (2022). Semi-supervised clustering under a \u201ccompact-cluster\u201d assumption. IEEE Transactions on Knowledge and Data Engineering, 35(5), 5244\u20135256.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"9489_CR17","doi-asserted-by":"publisher","first-page":"119733","DOI":"10.1016\/j.eswa.2023.119733","volume":"221","author":"Z Jiang","year":"2023","unstructured":"Jiang, Z., Zhao, L., Lu, Y., Zhan, Y., & Mao, Q. (2023a). A semi-supervised resampling method for class-imbalanced learning. Expert Systems with Applications, 221, 119733.","journal-title":"Expert Systems with Applications"},{"issue":"9","key":"9489_CR18","doi-asserted-by":"publisher","first-page":"e13377","DOI":"10.1111\/exsy.13377","volume":"40","author":"Z Jiang","year":"2023","unstructured":"Jiang, Z., Zhao, L., & Zhan, Y. (2023b). A boosted co-training method for class-imbalanced learning. Expert Systems, 40(9), e13377.","journal-title":"Expert Systems"},{"key":"9489_CR19","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409","author":"W-C Lin","year":"2017","unstructured":"Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017). Clustering-based undersampling in class-imbalanced data. Information Sciences, 409, 17\u201326.","journal-title":"Information Sciences"},{"issue":"10","key":"9489_CR20","doi-asserted-by":"publisher","first-page":"2469","DOI":"10.1109\/TPAMI.2017.2763945","volume":"40","author":"H Liu","year":"2017","unstructured":"Liu, H., Tao, Z., & Fu, Y. (2017). Partition level constrained clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2469\u20132483.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"57","key":"9489_CR21","first-page":"1","volume":"21","author":"F Ma","year":"2020","unstructured":"Ma, F., Meng, D., Dong, X., & Yang, Y. (2020). Self-paced multi-view co-training. Journal of Machine Learning Research, 21(57), 1\u201338.","journal-title":"Journal of Machine Learning Research"},{"key":"9489_CR22","unstructured":"Ma, F., Meng, D., Xie, Q., Li, Z., & Dong, X. (2017, 06\u201311 Aug). Self-paced co-training. In\u00a0D. Precup & Y. W. Teh (Eds.), Proceedings of the 34th international conference on machine learning (Vol. 70, pp.\u00a02275\u20132284).\u00a0PMLR.\u00a0https:\/\/proceedings.mlr.press\/v70\/ma17b.html"},{"issue":"1","key":"9489_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3366633","volume":"14","author":"Md Jan","year":"2019","unstructured":"Jan, Md., & Z., & Verma, B. (2019). Evolutionary classifier and cluster selection approach for ensemble classification. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 1\u201318.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"3","key":"9489_CR24","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s00357-019-09349-x","volume":"37","author":"I Melnykov","year":"2020","unstructured":"Melnykov, I., & Melnykov, V. (2020). A note on the formal implementation of the K-means algorithm with hard positive and negative constraints. Journal of Classification, 37(3), 789\u2013809.","journal-title":"Journal of Classification"},{"key":"9489_CR25","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.knosys.2017.12.006","volume":"143","author":"N Piroonsup","year":"2018","unstructured":"Piroonsup, N., & Sinthupinyo, S. (2018). Analysis of training data using clustering to improve semi-supervised self-training. Knowledge-Based Systems, 143, 65\u201380.","journal-title":"Knowledge-Based Systems"},{"issue":"287","key":"9489_CR26","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1080\/01621459.1959.10501526","volume":"54","author":"JW Pratt","year":"1959","unstructured":"Pratt, J. W. (1959). Remarks on zeros and ties in the Wilcoxon signed rank procedures. Journal of the American Statistical Association, 54(287), 655\u2013667.","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"9489_CR27","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/s00357-018-9285-7","volume":"36","author":"M Rashmi","year":"2019","unstructured":"Rashmi, M., & Sankaran, P. (2019). Optimal landmark point selection using clustering for manifold modeling and data classification. Journal of Classification, 36(1), 94\u2013112.","journal-title":"Journal of Classification"},{"key":"9489_CR28","doi-asserted-by":"crossref","unstructured":"Raskutti, B., Ferr\u00e1, H., & Kowalczyk, A. (2002). Combining clustering and co-training to enhance text classification using unlabelled data. In\u00a0Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 620\u2013625). Association for Computing Machinery.","DOI":"10.1145\/775047.775139"},{"key":"9489_CR29","doi-asserted-by":"publisher","first-page":"109121","DOI":"10.1016\/j.patcog.2022.109121","volume":"134","author":"R Sachdeva","year":"2023","unstructured":"Sachdeva, R., Cordeiro, F. R., Belagiannis, V., Reid, I., & Carneiro, G. (2023). ScanMix: Learning from severe label noise via semantic clustering and semi-supervised learning. Pattern Recognition, 134, 109121.","journal-title":"Pattern Recognition"},{"key":"9489_CR30","doi-asserted-by":"publisher","unstructured":"Sindhwani, V., & Rosenberg, D. S. (2008). An RKHS for multi-view learning and manifold co-regularization. In\u00a0Proceedings of the 25th international conference on machine learning\u00a0(pp. 976\u2013983). Association for Computing Machinery.\u00a0https:\/\/doi.org\/10.1145\/1390156.1390279","DOI":"10.1145\/1390156.1390279"},{"issue":"1","key":"9489_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2011.181","volume":"25","author":"Q Song","year":"2011","unstructured":"Song, Q., Ni, J., & Wang, G. (2011). A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, 25(1), 1\u201314.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"9489_CR32","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373\u2013440.","journal-title":"Machine Learning"},{"issue":"4","key":"9489_CR33","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1109\/TKDE.2011.28","volume":"24","author":"B Verma","year":"2011","unstructured":"Verma, B., & Rahman, A. (2011). Cluster-oriented ensemble classifier: Impact of multicluster characterization on ensemble classifier learning. IEEE Transactions on Knowledge and Data Engineering, 24(4), 605\u2013618.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"9489_CR34","unstructured":"Wagstaff, K., Cardie, C., Rogers, S., Schr\u00f6dl, S., et al. (2001). Constrained k-means clustering with background knowledge. In\u00a0Proceedings of the eighteenth international conference on machine learning\u00a0\u00a0(Vol. 1, pp.\u00a0577\u2013584)."},{"issue":"1","key":"9489_CR35","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TKDE.2014.2316512","volume":"27","author":"J Wu","year":"2014","unstructured":"Wu, J., Liu, H., Xiong, H., Cao, J., & Chen, J. (2014). K-means-based consensus clustering: A unified view. IEEE Transactions on Knowledge and Data Engineering, 27(1), 155\u2013169.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"9489_CR36","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.patcog.2008.07.010","volume":"42","author":"H Xue","year":"2009","unstructured":"Xue, H., Chen, S., & Yang, Q. (2009). Discriminatively regularized least-squares classification. Pattern Recognition, 42(1), 93\u2013104.","journal-title":"Pattern Recognition"},{"key":"9489_CR37","doi-asserted-by":"crossref","unstructured":"Ye, H.-J., Zhan, D.-C., Miao, Y., Jiang, Y., &\u00a0Zhou, Z.-H.\u00a0(2015). Rank consistency based multi-view learning: A privacy-preserving approach. In\u00a0Proceedings of the 24th ACM international on conference on Information and knowledge management\u00a0(pp.\u00a0991\u20131000).\u00a0Association for Computing Machinery.","DOI":"10.1145\/2806416.2806552"},{"issue":"12","key":"9489_CR38","doi-asserted-by":"publisher","first-page":"2394","DOI":"10.1109\/TKDE.2018.2818729","volume":"30","author":"Z Yu","year":"2018","unstructured":"Yu, Z., Luo, P., Liu, J., Wong, H.-S., You, J., Han, G., & Zhang, J. (2018). Semi-supervised ensemble clustering based on selected constraint projection. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2394\u20132407.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"9489_CR39","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s10115-012-0521-x","volume":"35","author":"S Zeng","year":"2013","unstructured":"Zeng, S., Tong, X., Sang, N., & Huang, R. (2013). A study on semi-supervised FCM algorithm. Knowledge and Information Systems, 35, 585\u2013612.","journal-title":"Knowledge and Information Systems"},{"issue":"6","key":"9489_CR40","doi-asserted-by":"publisher","first-page":"1612","DOI":"10.1109\/TSMCB.2011.2157998","volume":"41","author":"M-L Zhang","year":"2011","unstructured":"Zhang, M.-L., & Zhou, Z.-H. (2011). CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(6), 1612\u20131626.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"issue":"11","key":"9489_CR41","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","volume":"17","author":"Z-H Zhou","year":"2005","unstructured":"Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529\u20131541.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["Journal of Classification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-024-09489-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00357-024-09489-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-024-09489-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:10:44Z","timestamp":1742371844000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00357-024-09489-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["9489"],"URL":"https:\/\/doi.org\/10.1007\/s00357-024-09489-9","relation":{},"ISSN":["0176-4268","1432-1343"],"issn-type":[{"value":"0176-4268","type":"print"},{"value":"1432-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"22 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors comply with all ethical standards. No research involving human participants and\/or animals was conducted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}