{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:24:48Z","timestamp":1772605488844,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Multidimensional clustering of large-scale multi-view data is an important topic because it makes possible to combine a variety of manifestations of a complex information set. Nevertheless, comparing and selecting the most suitable deep clustering method is not an easy task, especially when several opposing criteria are applied. Multi-criteria decision-making (MCDM) techniques provide systematic approaches to making such judgments, although they are often limited in their ability to handle uncertainty, imprecise judgments, and interdependencies in practice. To solve these problems, this paper suggests a circular Fermatean fuzzy technique order preference by similarity to ideal solution (CFF-TOPSIS) method, which combines improved fuzzy modeling with MCDM to make the decision-making process accurate and sound. By exploiting the intrinsic symmetry of TOPSIS, where distances to positive and negative ideal solutions are treated symmetrically, the proposed model integrates five evaluation criteria for assessing clustering adequacy, including clustering accuracy, scalability, computational complexity, robustness, and interpretability, to critically evaluate five alternative clustering methods based on the input of three decision-makers. This measurement is performed efficiently by the CFF-TOPSIS method based on the uncertainty and subjective judgment contained within circular Fermatean fuzzy sets (CFFSs). The model is reliable and superior to existing models, as confirmed by sensitivity and comparative analyses. The suggested approach provides a systematic and flexible method for making decisions in complex big-data settings, while maintaining symmetry in the evaluation of alternatives and criteria.<\/jats:p>","DOI":"10.3390\/sym17081253","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T13:25:11Z","timestamp":1754486711000},"page":"1253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Fuzzy MCDM-Based Deep Multi-View Clustering Approach for Large-Scale Multi-View Data Analysis"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3679-6609","authenticated-orcid":false,"given":"Yueyao","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy Sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. 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