{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:17Z","timestamp":1754156837062,"version":"3.41.2"},"reference-count":42,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2024,7,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. <jats:italic>\u03c1<\/jats:italic> value (local density) and <jats:italic>\u03b4<\/jats:italic> value (the distance between a point and another point with a higher <jats:italic>\u03c1<\/jats:italic> value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher <jats:italic>\u03c1<\/jats:italic> value and a higher <jats:italic>\u03b4<\/jats:italic> value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-08-2021-0222","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T13:24:24Z","timestamp":1660656264000},"page":"380-406","source":"Crossref","is-referenced-by-count":1,"title":["TMsDP: two-stage density peak clustering based on multi-strategy optimization"],"prefix":"10.1108","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9005-0751","authenticated-orcid":false,"given":"Jie","family":"Ma","sequence":"first","affiliation":[]},{"given":"Zhiyuan","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Mo","family":"Hu","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"key2024072308212506300_ref001","doi-asserted-by":"crossref","first-page":"107589","DOI":"10.1016\/j.patcog.2020.107589","article-title":"DenMune: density peak based clustering using mutual nearest 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