{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:15:36Z","timestamp":1771697736054,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819984343","type":"print"},{"value":"9789819984350","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8435-0_3","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"31-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Unsupervised Concept Drift Detection via\u00a0Imbalanced Cluster Discriminator Learning"],"prefix":"10.1007","author":[{"given":"Mingjie","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yiqun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuzhu","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"\u017dliobait\u0117, I., Pechenizkiy, M., et\u00a0al.: An overview of concept drift applications. In: Big Data Analysis: New Algorithms for a New Society, pp. 91\u2013114 (2016)","DOI":"10.1007\/978-3-319-26989-4_4"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Cheung, Y.m., Zhang, Y.: Fast and accurate hierarchical clustering based on growing multilayer topology training. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 876\u2013890 (2018)","DOI":"10.1109\/TNNLS.2018.2853407"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, Y., Ji, Y., et\u00a0al.: Heterogeneous drift learning: classification of mix-attribute data with concept drifts. In: DSAA, pp. 1\u201310 (2022)","DOI":"10.1109\/DSAA54385.2022.10032342"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, Y., Zeng, A., et\u00a0al.: Time-series data imputation via realistic masking-guided tri-attention Bi-GRU. In: ECAI, pp.\u00a01\u20139 (2023)","DOI":"10.3233\/FAIA230625"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"3725","DOI":"10.1007\/s10462-020-09939-x","volume":"54","author":"\u00d6 G\u00f6z\u00fca\u00e7\u0131k","year":"2021","unstructured":"G\u00f6z\u00fca\u00e7\u0131k, \u00d6., Can, F.: Concept learning using one-class classifiers for implicit drift detection in evolving data streams. Artif. Intell. Rev. 54, 3725\u20133747 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"G\u00f6z\u00fca\u00e7\u0131k, \u00d6., B\u00fcy\u00fck\u00e7ak\u0131r, A., et\u00a0al.: Unsupervised concept drift detection with a discriminative classifier. In: CIKM, pp. 2365\u20132368 (2019)","DOI":"10.1145\/3357384.3358144"},{"key":"3_CR7","unstructured":"Frittoli, L., Carrera, D., et\u00a0al.: Nonparametric and online change detection in multivariate Datastreams using QuantTree. IEEE Trans. Knowl. Data Eng. 35(8), 8328\u20138342 (2023)"},{"issue":"6","key":"3_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1381","volume":"10","author":"RN Gemaque","year":"2020","unstructured":"Gemaque, R.N., Costa, A.F.J., et al.: An overview of unsupervised drift detection methods. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 10(6), e1381 (2020)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"issue":"12","key":"3_CR9","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu, J., Liu, A., et al.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Gama, J., Medas, P., et\u00a0al.: Learning with drift detection. In: Brazilian Symposium on Artificial Intelligence, pp. 286\u2013295 (2004)","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"3_CR11","unstructured":"Baena-Garc\u0131a, M., del Campo-\u00c1vila, J., et\u00a0al.: Early drift detection method. In: Fourth International Workshop Knowledge Discovery Data Streams, vol.\u00a06, pp. 77\u201386 (2006)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: SDM, pp. 443\u2013448 (2007)","DOI":"10.1137\/1.9781611972771.42"},{"issue":"6","key":"3_CR13","doi-asserted-by":"publisher","first-page":"3198","DOI":"10.1109\/TCYB.2020.2983962","volume":"51","author":"A Liu","year":"2021","unstructured":"Liu, A., Lu, J., et al.: Concept drift detection via equal intensity k-means space partitioning. IEEE Trans. Cybern. 51(6), 3198\u20133211 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"3_CR14","unstructured":"Dasu, T., Krishnan, S., et\u00a0al.: An information-theoretic approach to detecting changes in multi-dimensional data streams. In: Proceedings of 28th ISCAS (2006)"},{"key":"3_CR15","unstructured":"Boracchi, G., Carrera, D., et\u00a0al.: QuantTree: histograms for change detection in multivariate data streams. In: ICML, pp. 639\u2013648 (2018)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Friedman, J.H., Rafsky, L.C.: Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7(4), 697\u2013717 (1979)","DOI":"10.1214\/aos\/1176344722"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y.m.: A fast hierarchical clustering approach based on partition and merging scheme. In: ICACI, pp. 846\u2013851 (2018)","DOI":"10.1109\/ICACI.2018.8377573"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y.m., Liu, Y.: Quality preserved data summarization for fast hierarchical clustering. In: IJCNN, pp. 4139\u20134146 (2016)","DOI":"10.1109\/IJCNN.2016.7727739"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y.M.: Graph-based dissimilarity measurement for cluster analysis of any-type-attributed data. IEEE Trans. Neural Netw. Learn. Syst. 34, 6530\u20136544 (2022)","DOI":"10.1109\/TNNLS.2022.3202700"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y.m., Zeng, A.: Het2Hom: representation of heterogeneous attributes into homogeneous concept spaces for categorical-and-numerical-attribute data clustering. In: IJCAI, pp. 3758\u20133765 (2022)","DOI":"10.24963\/ijcai.2022\/522"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y.m.: Exploiting order information embedded in ordered categories for ordinal data clustering. In: ISMIS, pp. 247\u2013257 (2018)","DOI":"10.1007\/978-3-030-01851-1_24"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Shang, X., Lu, Y., et\u00a0al.: Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features. In: IJCAI, pp. 2218\u20132224 (2022)","DOI":"10.24963\/ijcai.2022\/308"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Shang, X., Lu, Y., et\u00a0al.: FEDIC: Federated learning on non-IID and long-tailed data via calibrated distillation. In: ICME, pp.\u00a01\u20136 (2022)","DOI":"10.1109\/ICME52920.2022.9860009"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Li, M., Cheung, Y.m., Lu, Y., et\u00a0al.: Long-tailed visual recognition via Gaussian clouded logit adjustment. In: CVPR, pp. 6929\u20136938 (2022)","DOI":"10.36227\/techrxiv.17031920.v1"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Lu, Y., Cheung, Y.m., et\u00a0al.: Adaptive chunk-based dynamic weighted majority for imbalanced data streams with concept drift. IEEE Trans. Neural Netw. Learn. Syst. 31(8), 2764\u20132778 (2020)","DOI":"10.1109\/TNNLS.2019.2951814"},{"issue":"3","key":"3_CR26","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s10115-021-01650-9","volume":"64","author":"J Tekli","year":"2022","unstructured":"Tekli, J.: An overview of cluster-based image search result organization: background, techniques, and ongoing challenges. Knowl. Inf. Syst. 64(3), 589\u2013642 (2022)","journal-title":"Knowl. Inf. Syst."},{"issue":"1","key":"3_CR27","first-page":"61","volume":"3","author":"C Aksoylar","year":"2016","unstructured":"Aksoylar, C., Qian, J., et al.: Clustering and community detection with imbalanced clusters. IEEE Trans. Signal Inf. Process Netw. 3(1), 61\u201376 (2016)","journal-title":"IEEE Trans. Signal Inf. Process Netw."},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Lu, Y., Cheung, Y.m., et\u00a0al.: Self-adaptive multiprototype-based competitive learning approach: a k-means-type algorithm for imbalanced data clustering. IEEE Trans. Cybern. 51(3), 1598\u20131612 (2019)","DOI":"10.1109\/TCYB.2019.2916196"},{"issue":"6191","key":"3_CR29","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. Science 344(6191), 1492\u20131496 (2014)","journal-title":"Science"},{"key":"3_CR30","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1007\/s10618-020-00698-5","volume":"34","author":"VM Souza","year":"2020","unstructured":"Souza, V.M., dos Reis, D.M., et al.: Challenges in benchmarking stream learning algorithms with real-world data. Data Min. Knowl. Disc. 34, 1805\u20131858 (2020)","journal-title":"Data Min. Knowl. Disc."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8435-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:08:53Z","timestamp":1703318933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8435-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984343","9789819984350"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8435-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}