{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:57:14Z","timestamp":1773482234986,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2308085MF220"],"award-info":[{"award-number":["2308085MF220"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"University Natural Science Research Projects of Anhui Province","award":["2022AH050972"],"award-info":[{"award-number":["2022AH050972"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"school-enterprise cooperation of Anhui Polytechnic University","award":["2023qyhz15"],"award-info":[{"award-number":["2023qyhz15"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"Anhui Future Technology Research Institute Enterprise Cooperation Project","award":["2023qyhz12"],"award-info":[{"award-number":["2023qyhz12"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]},{"name":"2024 Anhui Provincial Higher Education Research Project","award":["2024AH050109"],"award-info":[{"award-number":["2024AH050109"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s41060-025-00921-w","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T06:34:17Z","timestamp":1765262057000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel transfer learning method leveraging clustering and local variance for drifting data streams classification"],"prefix":"10.1007","volume":"21","author":[{"given":"Jian","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Sanmin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Subin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Liwei","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Huixian","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"issue":"10","key":"921_CR1","doi-asserted-by":"publisher","first-page":"9523","DOI":"10.1016\/j.jksuci.2021.11.006","volume":"34","author":"S Agrahari","year":"2022","unstructured":"Agrahari, S., Singh, A.K.: Concept drift detection in data stream mining: A literature review[J]. Journal of King Saud University-Computer and Information Sciences 34(10), 9523\u20139540 (2022)","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"issue":"1","key":"921_CR2","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10994-019-05840-z","volume":"109","author":"A Cano","year":"2020","unstructured":"Cano, A., Krawczyk, B.: Kappa updated ensemble for drifting data stream mining[J]. Mach. Learn. 109(1), 175\u2013218 (2020)","journal-title":"Mach. Learn."},{"key":"921_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2023.102232","volume":"148","author":"K Zhang","year":"2023","unstructured":"Zhang, K., Zhang, T., Liu, S.: A novel ensemble framework driven by diversity and cooperativity for non-stationary data stream classification[J]. Data & Knowledge Engineering 148, 102232 (2023)","journal-title":"Data & Knowledge Engineering"},{"issue":"1","key":"921_CR4","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TNNLS.2021.3091681","volume":"34","author":"S Liu","year":"2021","unstructured":"Liu, S., Xue, S., Wu, J., et al.: Online active learning for drifting data streams[J]. IEEE Transactions on Neural Networks and Learning Systems 34(1), 186\u2013200 (2021)","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"921_CR5","volume":"11","author":"M Bahri","year":"2021","unstructured":"Bahri, M., Bifet, A., Gama, J., et al.: Data stream analysis: Foundations, major tasks and tools[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11(3), e1405 (2021)","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"2","key":"921_CR6","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s10462-020-09874-x","volume":"54","author":"A Zubarolu","year":"2021","unstructured":"Zubarolu, A., Atalay, V.: Data stream clustering: a review[J]. Artif. Intell. Rev. 54(2), 1201\u20131236 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"921_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2021.113492","volume":"150","author":"B Baesens","year":"2021","unstructured":"Baesens, B., H\u00f6ppner, S., Verdonck, T.: Data engineering for fraud detection[J]. Decis. Support Syst. 150, 113492 (2021)","journal-title":"Decis. Support Syst."},{"issue":"1","key":"921_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone, N., Ngoc, T.N., Phai, V.D., et al.: A deep learning approach to network intrusion detection[J]. IEEE transactions on emerging topics in computational intelligence 2(1), 41\u201350 (2018)","journal-title":"IEEE transactions on emerging topics in computational intelligence"},{"key":"921_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2023.02.027","volume":"630","author":"D Leite","year":"2023","unstructured":"Leite, D., krjanc, I., Blai, S., et al.: Interval incremental learning of interval data streams and application to vehicle tracking[J]. Information Sciences 630, 1\u201322 (2023)","journal-title":"Information Sciences"},{"key":"921_CR10","doi-asserted-by":"crossref","unstructured":"Meng, Y.: Analysis of Performance Improvement of Real-time Internet of Things Application Data Processing in the Movie Industry Platform[J]. Computational Intelligence and Neuroscience, 2022, (2022)","DOI":"10.1155\/2022\/5237252"},{"key":"921_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102448","volume":"63","author":"F Yao","year":"2020","unstructured":"Yao, F., Wang, Y.: Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data[J]. Sustain. Cities Soc. 63, 102448 (2020)","journal-title":"Sustain. Cities Soc."},{"issue":"3","key":"921_CR12","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/JBHI.2021.3106387","volume":"26","author":"PM Kumar","year":"2021","unstructured":"Kumar, P.M., Hong, C.S., Afghah, F., et al.: Clouds proportionate medical data stream analytics for internet of things-based healthcare systems[J]. IEEE J. Biomed. Health Inform. 26(3), 973\u2013982 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"921_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494832","volume":"55","author":"C Fahy","year":"2022","unstructured":"Fahy, C., Yang, S., Gongora, M.: Scarcity of labels in non-stationary data streams: A survey[J]. ACM Computing Surveys (CSUR) 55(2), 1\u201339 (2022)","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"2","key":"921_CR14","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s10462-020-09874-x","volume":"54","author":"A Zubaro\u011flu","year":"2021","unstructured":"Zubaro\u011flu, A., Atalay, V.: Data stream clustering: a review[J]. Artif. Intell. Rev. 54(2), 1201\u20131236 (2021)","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"921_CR15","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.: A survey on semi-supervised learning[J]. Mach. Learn. 109(2), 373\u2013440 (2020)","journal-title":"Mach. Learn."},{"key":"921_CR16","unstructured":"Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning[M]. Springer Nature (2022)"},{"key":"921_CR17","doi-asserted-by":"crossref","unstructured":"Tyagi, K., Rane, C., Sriram, R., et al.: Unsupervised learning[M]\/\/Artificial intelligence and machine learning for edge computing. Academic Press, 33-52, (2022)","DOI":"10.1016\/B978-0-12-824054-0.00012-5"},{"key":"921_CR18","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.ins.2020.03.052","volume":"525","author":"SU Din","year":"2020","unstructured":"Din, S.U., Shao, J., Kumar, J., et al.: Online reliable semi-supervised learning on evolving data streams[J]. Inf. Sci. 525, 153\u2013171 (2020)","journal-title":"Inf. Sci."},{"key":"921_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119273","volume":"215","author":"G Liao","year":"2023","unstructured":"Liao, G., Zhang, P., Yin, H., et al.: A novel semi-supervised classification approach for evolving data streams[J]. Expert Syst. Appl. 215, 119273 (2023)","journal-title":"Expert Syst. Appl."},{"key":"921_CR20","doi-asserted-by":"crossref","unstructured":"Hwang, S.J., Choi, J.W., Choi, J.: Enhancing Active Learning with Semi-Supervised Loss Prediction Modules[J]. IEEE Access, (2024)","DOI":"10.1109\/ACCESS.2024.3449449"},{"key":"921_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119733","volume":"221","author":"Z Jiang","year":"2023","unstructured":"Jiang, Z., Zhao, L., Lu, Y., et al.: A semi-supervised resampling method for class-imbalanced learning[J]. Expert Syst. Appl. 221, 119733 (2023)","journal-title":"Expert Syst. Appl."},{"key":"921_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106749","volume":"215","author":"X Zheng","year":"2021","unstructured":"Zheng, X., Li, P., Hu, X., et al.: Semi-supervised classification on data streams with recurring concept drift and concept evolution[J]. Knowl.-Based Syst. 215, 106749 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"921_CR23","first-page":"1","volume":"21","author":"SJ Pan","year":"2020","unstructured":"Pan, S.J.: Transfer learning[J]. Learning 21, 1\u20132 (2020)","journal-title":"Learning"},{"issue":"2","key":"921_CR24","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/JAS.2022.106004","volume":"10","author":"W Zhang","year":"2022","unstructured":"Zhang, W., Deng, L., Zhang, L., et al.: A survey on negative transfer[J]. IEEE\/CAA Journal of Automatica Sinica 10(2), 305\u2013329 (2022)","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"12","key":"921_CR25","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu, J., Liu, A., Dong, F., et al.: Learning under concept drift: A review[J]. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"921_CR26","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/ACCESS.2018.2886026","volume":"7","author":"AS Iwashita","year":"2018","unstructured":"Iwashita, A.S., Papa, J.P.: An overview on concept drift learning[J]. IEEE access 7, 1532\u20131547 (2018)","journal-title":"IEEE access"},{"key":"921_CR27","first-page":"145","volume":"2021","author":"N Agarwal","year":"2020","unstructured":"Agarwal, N., Sondhi, A., Chopra, K., et al.: Transfer learning: Survey and classification[J]. Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS 2021, 145\u2013155 (2020)","journal-title":"Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS"},{"key":"921_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-019-1433-0","volume":"1","author":"S Wares","year":"2019","unstructured":"Wares, S., Isaacs, J., Elyan, E.: Data stream mining: methods and challenges for handling concept drift[J]. SN Applied Sciences 1, 1\u201319 (2019)","journal-title":"SN Applied Sciences"},{"key":"921_CR29","unstructured":"Golab, L., Ozsu, M.T.: Data stream management[M]. Springer Nature, (2022)"},{"key":"921_CR30","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neucom.2021.04.112","volume":"459","author":"SCH Hoi","year":"2021","unstructured":"Hoi, S.C.H., Sahoo, D., Lu, J., et al.: Online learning: A comprehensive survey[J]. Neurocomputing 459, 249\u2013289 (2021)","journal-title":"Neurocomputing"},{"key":"921_CR31","doi-asserted-by":"crossref","unstructured":"Patel, P., Sivaiah, B., Patel, R.: Approaches for finding optimal number of clusters using k-means and agglomerative hierarchical clustering techniques[C]\/\/2022 international conference on intelligent controller and computing for smart power (ICICCSP). IEEE, 1-6 (2022)","DOI":"10.1109\/ICICCSP53532.2022.9862439"},{"key":"921_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116510","volume":"193","author":"M Jain","year":"2022","unstructured":"Jain, M., Kaur, G., Saxena, V.: A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection[J]. Expert Syst. Appl. 193, 116510 (2022)","journal-title":"Expert Syst. Appl."},{"key":"921_CR33","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1016\/j.ins.2022.06.013","volume":"607","author":"A Degirmenci","year":"2022","unstructured":"Degirmenci, A., Karal, O.: Efficient density and cluster based incremental outlier detection in data streams[J]. Inf. Sci. 607, 901\u2013920 (2022)","journal-title":"Inf. Sci."},{"key":"921_CR34","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2018.09.004","volume":"321","author":"S Jiang","year":"2018","unstructured":"Jiang, S., Yonghui, X., Song, H., Qingyao, W., Ng, M.K., Min, H., Qiu, S.: Multi-instance transfer metric learning by weighted distribution and consistent maximum likelihood estimation. Neurocomputing 321, 49\u201360 (2018)","journal-title":"Neurocomputing"},{"issue":"1","key":"921_CR35","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/TCYB.2020.2969689","volume":"52","author":"Q Chen","year":"2020","unstructured":"Chen, Q., Xue, B., Zhang, M.: Genetic programming for instance transfer learning in symbolic regression[J]. IEEE Transactions on Cybernetics 52(1), 25\u201338 (2020)","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"921_CR36","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1109\/TCSVT.2019.2900467","volume":"30","author":"Z Peng","year":"2019","unstructured":"Peng, Z., Zhang, W., Han, N., et al.: Active transfer learning[J]. IEEE Trans. Circuits Syst. Video Technol. 30(4), 1022\u20131036 (2019)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"921_CR37","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhou, Q., Xue, Y., et al.: Transfer learning for semi-supervised classification of non-stationary data streams[C]\/\/Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 1822, 2020, Proceedings, Part V 27. Springer International Publishing, 468-477, (2020)","DOI":"10.1007\/978-3-030-63823-8_54"},{"key":"921_CR38","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.eswa.2017.10.003","volume":"92","author":"J Dem\u0161ar","year":"2018","unstructured":"Dem\u0161ar, J., Bosni\u0107, Z.: Detecting concept drift in data streams using model explanation[J]. Expert Syst. Appl. 92, 546\u2013559 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"921_CR39","first-page":"1","volume":"12","author":"J Xuan","year":"2020","unstructured":"Xuan, J., Lu, J., Zhang, G.: Bayesian nonparametric unsupervised concept drift detection for data stream mining[J]. ACM Transactions on Intelligent Systems and Technology (TIST) 12(1), 1\u201322 (2020)","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"issue":"3","key":"921_CR40","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1007\/s10462-022-10232-2","volume":"56","author":"EB Gulcan","year":"2023","unstructured":"Gulcan, E.B., Can, F.: Unsupervised concept drift detection for multi-label data streams[J]. Artif. Intell. Rev. 56(3), 2401\u20132434 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"921_CR41","unstructured":"Jiao, B., Guo, Y., Gong, D., et al.: Dynamic ensemble selection for imbalanced data streams with concept drift[J]. IEEE transactions on neural networks and learning systems, (2022)"},{"issue":"6191","key":"921_CR42","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks[J]. Science. 344(6191), 1492\u20131496 (2014)","journal-title":"Science."},{"issue":"8","key":"921_CR43","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed, M., Seraj, R., Islam, S.M.S.: The k-means algorithm: A comprehensive survey and performance evaluation[J]. Electronics 9(8), 1295 (2020)","journal-title":"Electronics"},{"issue":"11","key":"921_CR44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206832","volume":"13","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Liu, N., Wang, S.: A differential privacy protecting K-means clustering algorithm based on contour coefficients[J]. PLoS ONE 13(11), e0206832 (2018)","journal-title":"PLoS ONE"},{"issue":"2","key":"921_CR45","first-page":"896","volume":"3","author":"DH Lee","year":"2013","unstructured":"Lee, D.H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks[C]\/\/Workshop on challenges in representation learning. ICML. 3(2), 896 (2013)","journal-title":"ICML."},{"key":"921_CR46","unstructured":"Erhan, D., Courville, A., Bengio, Y., et al.: Why does unsupervised pre-training help deep learning?[C]\/\/Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 201-208, (2010)"},{"key":"921_CR47","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks[C]\/\/Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315-323, (2011)"},{"key":"921_CR48","doi-asserted-by":"crossref","unstructured":"Grandvalet, Y., Bengio, Y.: Entropy regularization[J]. (2006)","DOI":"10.7551\/mitpress\/6173.003.0013"},{"key":"921_CR49","doi-asserted-by":"crossref","unstructured":"Min, Z., Bai, J., Li, C.: Leveraging Local Variance for Pseudo-Label Selection in Semi-supervised Learning[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 38(13), 14370-14378 (2024)","DOI":"10.1609\/aaai.v38i13.29350"},{"key":"921_CR50","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"2000","author":"MM Breunig","year":"2000","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., et al.: LOF: identifying density-based local outliers[C]\/\/Proceedings of the. ACM SIGMOD international conference on Management of data. 2000, 93\u2013104 (2000)","journal-title":"ACM SIGMOD international conference on Management of data."},{"key":"921_CR51","unstructured":"Bifet, A., Holmes, G., Pfahringer, B., et al.: Moa: Massive online analysis, a framework for stream classification and clustering[C]\/\/Proceedings of the first workshop on applications of pattern analysis. PMLR, 44-50, (2010)"},{"issue":"1","key":"921_CR52","doi-asserted-by":"publisher","first-page":"438","DOI":"10.2991\/ijcis.11.1.33","volume":"11","author":"F Dong","year":"2018","unstructured":"Dong, F., Lu, J., Zhang, G., et al.: Active fuzzy weighting ensemble for dealing with concept drift[J]. International Journal of Computational Intelligence Systems 11(1), 438\u2013450 (2018)","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"921_CR53","doi-asserted-by":"crossref","unstructured":"Huan, Z., Wang, Y., He, Y., et al.: Learning to select instance: Simultaneous transfer learning and clustering[C]\/\/Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 1950-1954, (2021)","DOI":"10.1145\/3404835.3462992"},{"key":"921_CR54","doi-asserted-by":"crossref","unstructured":"Su\u00e1rez-Cetrulo, A.L., Quintana, D., Cervantes, A.: A survey on machine learning for recurring concept drifting data streams[J]. Expert Syst. Appl. 213, 118934 (2023)","DOI":"10.1016\/j.eswa.2022.118934"},{"key":"921_CR55","unstructured":"Xia, R., Hu, X., Lu, J., et al.: Instance selection and instance weighting for cross-domain sentiment classification via PU learning[C]\/\/Twenty-Third International Joint Conference on Artificial Intelligence. (2013)"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00921-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-025-00921-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00921-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:36:42Z","timestamp":1773481002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-025-00921-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["921"],"URL":"https:\/\/doi.org\/10.1007\/s41060-025-00921-w","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"assertion":[{"value":"13 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have no potential conflict of interest related to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"69"}}