{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T22:55:28Z","timestamp":1782168928793,"version":"3.54.5"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Chinese Agricultural Research System","award":["CARS-29"],"award-info":[{"award-number":["CARS-29"]}]},{"name":"Key Laboratory of Viticulture and Enology, Ministry of Agriculture, PR"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination; the roulette operator is used to select excellent particles; then, the selected particles are crossed according to their adaptive crossover probabilities; when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means\u2019 sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab206","type":"journal-article","created":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T20:09:08Z","timestamp":1639858148000},"page":"941-962","source":"Crossref","is-referenced-by-count":29,"title":["Customer Segmentation Using K-Means Clustering and the Hybrid Particle Swarm Optimization Algorithm"],"prefix":"10.1093","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1392-2074","authenticated-orcid":false,"given":"Yue","family":"Li","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering , , Beijing 100083, China"},{"name":"China Agricultural University , , Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5526-3583","authenticated-orcid":false,"given":"Jianfang","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering , , Beijing 100083, China"},{"name":"China Agricultural University , , Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2370-4038","authenticated-orcid":false,"given":"Xiaoquan","family":"Chu","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering , , Beijing 100083, China"},{"name":"China Agricultural University , , Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2063-7933","authenticated-orcid":false,"given":"Weisong","family":"Mu","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering , , Beijing 100083, China"},{"name":"China Agricultural University , , Beijing 100083, China"},{"name":"Key Laboratory of Viticulture and Enology , , Beijing 100083, China"},{"name":"Ministry of Agriculture , , Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"key":"2023041809374699600_","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.knosys.2017.06.018","article-title":"GMDH-based semi-supervised feature selection for customer classification","volume":"132","author":"Xiao","year":"2017","journal-title":"Knowledge-Based Syst."},{"key":"2023041809374699600_","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1016\/j.asoc.2017.02.004","article-title":"Clustering retail products based on customer behavior","volume":"60","author":"Hol\u00fd","year":"2017","journal-title":"Appl. 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