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Thirdly, the merging indicator is jointly defined by the local density of micro-clusters and the distance between micro-clusters, and the pairs of micro-clusters that satisfy the maximum merging indicator will be iteratively merged in a bottom-up hierarchical manner to obtain the final cluster structure. In addition, noisy data can be identified by analyzing the characteristics of the minimum spanning tree. Finally, the remaining samples are assigned to the cluster nearest to them. Extensive experiments were conducted on twenty-four datasets, we compared the MSC-WMST algorithm with the state-of-the-art algorithms. The experimental results demonstrate that MSC-WMST exhibits excellent performance in three evaluation metrics.<\/jats:p>","DOI":"10.1007\/s41019-024-00280-9","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T18:41:42Z","timestamp":1743878502000},"page":"376-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Micro-cluster Structure Clustering Based on Weight-Constrained Minimum Spanning Tree"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-1297","authenticated-orcid":false,"given":"Jiaman","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqi","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4703-780X","authenticated-orcid":false,"given":"Shaojie","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"key":"280_CR1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.patcog.2018.01.006","volume":"78","author":"W Zhang","year":"2018","unstructured":"Zhang W, Du L, Li L, Zhang X, Liu H (2018) Infinite bayesian one-class support vector machine based on dirichlet process mixture clustering. 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