{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:31:50Z","timestamp":1754152310520,"version":"3.41.2"},"reference-count":38,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773150"],"award-info":[{"award-number":["61773150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2018201115"],"award-info":[{"award-number":["F2018201115"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Scientific Research Foundation of Education Department of Hebei Province","award":["ZD2019021"],"award-info":[{"award-number":["ZD2019021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Found. Comput. Sci."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p> Unsupervised image clustering is a challenging task in computer vision. Recently, various deep clustering algorithms based on contrastive learning have achieved promising performance and some distinguishable features representation were obtained only by taking different augmented views of same image as positive pairs and maximizing their similarities, whereas taking other images\u2019 augmentations in the same batch as negative pairs and minimizing their similarities. However, due to the fact that there is more than one image in a batch belong to the same class, simply pushing the negative instances apart will result in inter-class conflictions and lead to the clustering performance degradation. In order to solve this problem, we propose a deep clustering algorithm based on supported nearest neighbors (SNDC), which constructs positive pairs of current images by maintaining a support set and find its k nearest neighbors from the support set. By going beyond single instance positive, SNDC can learn more generalized features representation with inherent semantic meaning and therefore alleviating inter-class conflictions. Experimental results on multiple benchmark datasets show that the performance of SNDC is superior to the state-of-the-art clustering models, with accuracy improvement of 6.2% and 20.5% on CIFAR-10 and ImageNet-Dogs respectively. <\/jats:p>","DOI":"10.1142\/s0129054122460017","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T21:11:41Z","timestamp":1675890701000},"page":"599-618","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Image Clustering Algorithm Based on Supported Nearest Neighbors"],"prefix":"10.1142","volume":"36","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"Hebei Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1699-2115","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of 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