{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:07:57Z","timestamp":1761646077550,"version":"3.37.3"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572225"],"award-info":[{"award-number":["61572225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2019,7]]},"abstract":"<jats:p> The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases. <\/jats:p>","DOI":"10.1142\/s0218001419500125","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T03:56:28Z","timestamp":1543377388000},"page":"1950012","source":"Crossref","is-referenced-by-count":11,"title":["HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-2922","authenticated-orcid":false,"given":"Jianhua","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Management, Jilin University, 5988 Renming Street, Changchun 130022, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York, Science Hall 249, New Paltz, New York 12561, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,6,25]]},"reference":[{"key":"S0218001419500125BIB001","doi-asserted-by":"crossref","unstructured":"E. Aksehirli,  B. Goethals and  E. 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