{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:34:53Z","timestamp":1777696493725,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2023,10,6]]},"abstract":"<jats:p>Graph-based clustering performs efficiently for identifying clusters in local and nonlinear data Patterns. The existing methods face the problem of parameter selection, such as the setting of k of the k-nearest neighbor graph and the threshold in noise detection. In this paper, a non-parametric clustering algorithm (NonPC) is proposed to tackle those inherent limitations and improve clustering performance. The weighted natural neighbor graph (wNaNG) is developed to represent the given data without any prior knowledge. What is more, the proposed NonPC method adaptively detects noise data in an unsupervised way based on some attributes extracted from wNaNG. The algorithm works without preliminary parameter settings while automatically identifying clusters with unbalanced densities, arbitrary shapes, and noises. To assess the advantages of the NonPC algorithm, extensive experiments have been conducted compared with some classic and recent clustering methods. The results demonstrate that the proposed NonPC algorithm significantly outperforms the state-of-the-art and well-known algorithms in Adjusted Rand index, Normalized Mutual Information, and Fowlkes-Mallows index aspects.<\/jats:p>","DOI":"10.3233\/ida-220427","type":"journal-article","created":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T15:06:24Z","timestamp":1691939184000},"page":"1347-1358","source":"Crossref","is-referenced-by-count":3,"title":["NonPC: Non-parametric clustering algorithm with adaptive noise detecting"],"prefix":"10.1177","volume":"27","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Banan, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Shapingba, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyun","family":"Song","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Banan, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/IDA-220427_ref1","doi-asserted-by":"crossref","first-page":"107784","DOI":"10.1016\/j.patcog.2020.107784","article-title":"Type-reduced vague possibilistic fuzzy clustering for medical images","volume":"112","author":"Bose","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-220427_ref2","doi-asserted-by":"crossref","first-page":"107683","DOI":"10.1016\/j.patcog.2020.107683","article-title":"Robust semi-supervised nonnegative matrix factorization for image clustering","volume":"111","author":"Peng","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-220427_ref3","doi-asserted-by":"crossref","first-page":"108287","DOI":"10.1016\/j.patcog.2021.108287","article-title":"Spatio-Temporal association rule based deep annotation-free clustering (STAR-DAC) for unsupervised person re-identification","volume":"122","author":"Sridhar","year":"2022","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-220427_ref5","doi-asserted-by":"crossref","first-page":"108690","DOI":"10.1016\/j.patcog.2022.108690","article-title":"Graph regularization multidimensional projection","volume":"129","author":"Dal Col","year":"2022","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-220427_ref7","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.patrec.2016.05.007","article-title":"Natural neighbor: A self-adaptive neighborhood method without parameter K","volume":"80","author":"Zhu","year":"2016","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/IDA-220427_ref8","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1109\/TIP.2022.3187563","article-title":"Hierarchical Superpixel Segmentation by Parallel CRTrees Labeling","volume":"31","author":"Yan","year":"2022","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.3233\/IDA-220427_ref9","doi-asserted-by":"crossref","unstructured":"A. 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