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Syst."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The density peaks clustering (DPC) algorithm is a classical and widely used clustering method. However, the DPC algorithm requires manual selection of cluster centers, a single way of density calculation, and cannot effectively handle low-density points. To address the above issues, we propose a novel density deviation multi-peaks automatic clustering method (AmDPC) in this paper. Firstly, we propose a new local-density and use the deviation to measure the relationship between data points and the cut-off distance (<jats:inline-formula><jats:alternatives><jats:tex-math>$$d_c$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>d<\/mml:mi>\n                    <mml:mi>c<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>). Secondly, we divide the density deviation into multiple density levels equally and extract the points with higher distances in each density level. Finally, for the multi-peak points with higher distances at low-density levels, we merge them according to the size difference of the density deviation. We finally achieve the overall automatic clustering by processing the low-density points. To verify the performance of the method, we test the synthetic dataset, the real-world dataset, and the Olivetti Face dataset, respectively. The simulation experimental results indicate that the AmDPC method can handle low-density points more effectively and has certain effectiveness and robustness.<\/jats:p>","DOI":"10.1007\/s40747-022-00798-3","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T03:31:49Z","timestamp":1656041509000},"page":"177-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A novel density deviation multi-peaks automatic clustering algorithm"],"prefix":"10.1007","volume":"9","author":[{"given":"Wei","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0576-3950","authenticated-orcid":false,"given":"Limin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xuming","family":"Han","sequence":"additional","affiliation":[]},{"given":"Milan","family":"Parmar","sequence":"additional","affiliation":[]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"798_CR1","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","volume":"275","author":"CL Philip Chen","year":"2014","unstructured":"Philip Chen CL, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. 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