{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T05:23:59Z","timestamp":1782624239534,"version":"3.54.5"},"reference-count":42,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>A fundamental problem in machine learning is ensemble clustering, that is, combining multiple base clusterings to obtain improved clustering result. However, most of the existing methods are unsuitable for large-scale ensemble clustering tasks owing to efficiency bottlenecks. In this paper, we propose a large-scale spectral ensemble clustering (LSEC) method to balance efficiency and effectiveness. In LSEC, a large-scale spectral clustering-based efficient ensemble generation framework is designed to generate various base clusterings with low computational complexity. Thereafter, all the base clusterings are combined using a bipartite graph partition-based consensus function to obtain improved consensus clustering results. The LSEC method achieves a lower computational complexity than most existing ensemble clustering methods. Experiments conducted on ten large-scale datasets demonstrate the efficiency and effectiveness of the LSEC method. The MATLAB code of the proposed method and experimental datasets are available at https:\/\/github.com\/Li-Hongmin\/MyPaperWithCode.<\/jats:p>","DOI":"10.3233\/ida-216240","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T11:16:19Z","timestamp":1675163779000},"page":"59-77","source":"Crossref","is-referenced-by-count":11,"title":["LSEC: Large-scale spectral ensemble clustering"],"prefix":"10.1177","volume":"27","author":[{"given":"Hongmin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiucai","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Imakura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuya","family":"Sakurai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/IDA-216240_ref2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0168-1699(99)00046-0","article-title":"Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables","volume":"24","author":"Blackard","year":"1999","journal-title":"Computers and Electronics in Agriculture"},{"issue":"8","key":"10.3233\/IDA-216240_ref3","first-page":"1669","article-title":"Large scale spectral clustering via landmark-based sparse representation","volume":"45","author":"Cai","year":"2014","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"1","key":"10.3233\/IDA-216240_ref4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00778-010-0189-3","article-title":"Speed up kernel discriminant analysis","volume":"20","author":"Cai","year":"2011","journal-title":"The VLDB Journal"},{"issue":"8","key":"10.3233\/IDA-216240_ref5","first-page":"1548","article-title":"Graph regularized nonnegative matrix factorization for data representation","volume":"33","author":"Cai","year":"2010","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/IDA-216240_ref6","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDA-216240_ref7","doi-asserted-by":"crossref","unstructured":"X.Z. 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