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It focuses on clustering-based subset selection to improve data fusion performance. Five clustering methods \u2014 two K-means variants, Agglomerative Hierarchical (AH) clustering, BIRCH, and Chameleon \u2014 are evaluated for selecting optimal subsets of information retrieval systems. Experiments are conducted on two health-related datasets from the TREC challenge. The selected subsets are used in data fusion to boost retrieval quality and credibility. AH and BIRCH outperform other methods in identifying effective IR subsets. Using AH-based fusion of up to 20 systems results in a 60% gain in MAP and over a 30% increase in NDCG_UCC, a credibility-focused metric, compared to the best single system. Clustering-based fusion strategies significantly enhance the retrieval of trustworthy health content, helping to reduce misinformation. These findings support incorporating advanced data fusion into health information retrieval systems to improve access to reliable information. The source code of this research is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Gary752752\/DataFusion\">https:\/\/github.com\/Gary752752\/DataFusion<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/14604582251388860","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T12:11:33Z","timestamp":1760616693000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Combating health misinformation with fusion-based credible retrieval techniques"],"prefix":"10.1177","volume":"31","author":[{"given":"Yidong","family":"Huang","sequence":"first","affiliation":[{"name":"Engineering & Technical College of Chengdu University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2008-1736","authenticated-orcid":false,"given":"Shengli","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Ulster"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Lu","sequence":"additional","affiliation":[{"name":"Jiangsu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Geng","sequence":"additional","affiliation":[{"name":"Jiangsu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[{"name":"University of Ulster"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-040119-094127"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30461-X"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-021-01056-1"},{"key":"e_1_3_5_5_2","doi-asserted-by":"crossref","unstructured":"Zhang B Naderi N Jaume-Santero F et al.Ds4dh at TREC health misinformation 2021: multi-dimensional ranking models with transfer learning and rank fusion. arXiv preprint arXiv:220206771 2022.","DOI":"10.6028\/NIST.SP.500-335.misinfo-DigiLab"},{"key":"e_1_3_5_6_2","first-page":"10","article-title":"The promise of digital health: then, now, and the future","volume":"2022","author":"Abernethy A","year":"2022","unstructured":"Abernethy A, Adams L, Barrett M, et al. 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