{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:47:37Z","timestamp":1747190857211,"version":"3.40.5"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Foundation Project of China","award":["61962054"],"award-info":[{"award-number":["61962054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2022,1,11]]},"abstract":"<jats:p>Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.<\/jats:p>","DOI":"10.1155\/2022\/4176101","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:20:17Z","timestamp":1641932417000},"page":"1-13","source":"Crossref","is-referenced-by-count":2,"title":["A Fast Density Peak Clustering Method with Autoselect Cluster Centers"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0437-2453","authenticated-orcid":true,"given":"Zhihe","family":"Wang","sequence":"first","affiliation":[{"name":"The School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8606-9716","authenticated-orcid":true,"given":"Yongbiao","family":"Li","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8474-2591","authenticated-orcid":true,"given":"Hui","family":"Du","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-6737","authenticated-orcid":true,"given":"Xiaofen","family":"Wei","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2005.845141"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-015-0040-1"},{"key":"3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/3-540-28349-8_2","article-title":"A survey of clustering data mining techniques","volume-title":"Grouping Multidimensional Data","author":"P. 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