{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T03:16:38Z","timestamp":1785813398339,"version":"3.56.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021ZD0112904"],"award-info":[{"award-number":["2021ZD0112904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Clustering ensemble, which aims to learn a robust consensus clustering from multiple weak base clusterings, has achieved promising performance on various applications. With the development of big data, the scale and complexity of data is constantly increasing. However, most existing clustering ensemble methods typically employ shallow clustering algorithms to generate base clusterings. When confronted with high-dimensional complex data, these shallow algorithms fail to fully utilize the intricate features present in the latent data space. As a result, the quality and diversity of the generated base clusterings are insufficient, thus affecting the subsequent ensemble performance. To address this issue, we propose a novel clustering ensemble algorithm for handling deep embeddings using cluster confidence (CEDECC) to improve the robustness and performance. Instead of simply combining deep clustering with clustering ensembles, we take into consideration that the performance of existing deep clustering methods heavily relies on the quality of low-dimensional embeddings generated during the pre-training stage. The quality of embeddings is unstable due to the influence of different initialization parameters. In CEDECC, specifically, we first construct a cluster confidence measure to evaluate the quality of low-dimensional embeddings. Typically, high-quality low-dimensional embeddings yield accurate clustering results with the same model parameters. Then, we utilize multiple high-quality embeddings to generate the base partitions. In the ensemble strategy phase, we consider the cluster-wise diversity and propose a novel ensemble cluster estimation to improve the overall consensus performance of the model. Extensive experiments on three benchmark datasets and four real-world biological datasets have demonstrated that the proposed CEDECC consistently outperforms the state-of-the-art clustering ensemble methods.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae101","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T11:24:50Z","timestamp":1728905090000},"page":"163-174","source":"Crossref","is-referenced-by-count":6,"title":["A clustering ensemble algorithm for handling deep embeddings using cluster confidence"],"prefix":"10.1093","volume":"68","author":[{"given":"Lingbin","family":"Zeng","sequence":"first","affiliation":[{"name":"National University of Defense Technology , 109 Deya Road, Kaifu District, Changsha City, Hunan Province 410073 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixin","family":"Yao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology , 109 Deya Road, Kaifu District, Changsha City, Hunan Province 410073 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology , 109 Deya Road, Kaifu District, Changsha City, Hunan Province 410073 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liquan","family":"Xiao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology , 109 Deya Road, Kaifu District, Changsha City, Hunan Province 410073 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Qian","sequence":"additional","affiliation":[{"name":"National University of Defense Technology , 109 Deya Road, Kaifu District, Changsha City, Hunan Province 410073 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"2025021705263832800_ref1","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-981-16-5036-9_30","article-title":"Trends and future perspective challenges in big data","volume-title":"Proceeding of the 6th Euro-China Conference on Intelligent Data Analysis and Applications (ECC)","author":"Naeem","year":"2022"},{"key":"2025021705263832800_ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-55639-5","volume-title":"Big Data Analytics: Theory, Techniques, Platforms, and Applications","author":"Demirbaga","year":"2024"},{"key":"2025021705263832800_ref3","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.ins.2019.05.053","article-title":"Efficient privacy preservation of big data for accurate data mining","volume":"527","author":"Chamikara","year":"2020","journal-title":"Inf. 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