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Recently, multi-view clustering has received widespread attention because it can better use different views\u2019 consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.<\/jats:p>","DOI":"10.1007\/s10462-024-10785-4","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T18:01:28Z","timestamp":1716573688000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["The methods for improving large-scale multi-view clustering efficiency: a survey"],"prefix":"10.1007","volume":"57","author":[{"given":"Zengbiao","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihua","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"10785_CR1","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107522","volume":"108","author":"S Affeldt","year":"2020","unstructured":"Affeldt S, Labiod L, Nadif M (2020) Spectral clustering via ensemble deep autoencoder learning (SC-EDAE). 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