{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:35:47Z","timestamp":1752230147642,"version":"3.37.3"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:00:00Z","timestamp":1709337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019\u00a0M651844","BY2020241"],"award-info":[{"award-number":["2019\u00a0M651844","BY2020241"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Maritime Institute","award":["016102"],"award-info":[{"award-number":["016102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Density peaks clustering (DPC) is a relatively new density clustering algorithm. It is based on the idea that cluster centers always have relatively high local densities and are relatively far from the points with higher densities. With the aforementioned idea, a decision graph can be drawn, and cluster centers will be chosen easily with the aid of the decision graph. However, the algorithm has its own weaknesses. Because the algorithm calculates local density and allocates points based on the distances between certain points, the algorithm has difficulty in classifying points into proper groups with varying densities or nested structures. This paper proposes an improved density peaks clustering algorithm called Dratio-DPC to overcome this weakness. First, Dratio-DPC adjusts the original local density with a coefficient calculated with the density ratio. Second, Dratio-DPC takes density similarity into consideration to calculate the distances between one point and other points with higher local densities. We design and perform experiments on different benchmark datasets and compare the clustering results of Dratio-DPC, traditional clustering algorithms and three improved DPC algorithms. Comparison results show that Dratio-DPC is effective and applicable to a wider range of scenarios.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae022","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T17:27:06Z","timestamp":1709573226000},"page":"2515-2528","source":"Crossref","is-referenced-by-count":2,"title":["An Improved Density Peaks Clustering Algorithm Based On Density Ratio"],"prefix":"10.1093","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1837-7979","authenticated-orcid":false,"given":"Yujuan","family":"Zou","sequence":"first","affiliation":[{"name":"College of Computer and Information, Hohai University , Focheng West Road, Jiangning District, Nanjing 211100 , China"},{"name":"College of Information Engineering, Jiangsu Maritime Institute , Gezhi Road, Jiangning District, Nanjing 211199 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University , Focheng West Road, Jiangning District, Nanjing 211100 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University , Focheng West Road, Jiangning District, Nanjing 211100 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1994-9841","authenticated-orcid":false,"given":"Taizhi","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Research and Development, Nanjing Longyuan Microelectronic Company Limited , Nanyou Road, Jiangning District, Nanjing 211106 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"key":"2024072105012603400_ref1","volume-title":"Data Mining: Concepts and Techniques","author":"Han","year":"2011","edition":"3rd"},{"key":"2024072105012603400_ref2","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.patcog.2016.07.007","article-title":"Density-ratio based clustering for discovering clusters with varying densities","volume":"60","author":"Zhu","year":"2016","journal-title":"Pattern Recognit."},{"key":"2024072105012603400_ref3","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1093\/comjnl\/bxz017","article-title":"An improved seed point selection-based unsupervised color clustering for content-based image retrieval application","volume":"63","author":"Pavithra","year":"2020","journal-title":"Comput. 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