Application of PSO-integrated K-means algorithm in resident digital portrait classification

• Published in: PloS one Vol. 20; no. 8; p. e0329123
• Main Authors: Yue, Hongwei, Zhang, Hejuan, Dai, Yuqiao
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.08.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptability; Algorithms; Behavior; Biology and Life Sciences; Centroids; Classification; Cluster analysis; Clustering; Comparative analysis; Computer and Information Sciences; Efficiency; Global optimization; Income; Literature reviews; Low income groups; Mathematical optimization; Mutation; Optimization algorithms; Particle swarm optimization; Performance evaluation; Physical Sciences; Public administration; Research and Analysis Methods; Service enhancement; Social networks; Social organization; Social Sciences; Technology application; Vector quantization; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0329123

As digital governance progresses rapidly, constructing digital portraits of residents has become instrumental in enhancing local-level administrative capabilities. Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. Drawing on comprehensive resident information collected in 2023 from a community management system, the method leverages PSO’s global optimization abilities alongside K-means’ iterative refinement to dynamically update cluster centroids. Performance evaluation shows a significant uplift in clustering metrics, with a silhouette score of 0.752 ± 0.021 and inter-cluster distance of 1.493 ± 0.036. Comparative analysis against conventional and advanced methods (e.g., GA-K-means, DBSCAN) reveals that PSO-KM delivers superior outcomes. Among different feature categories, behavioral data yield the best classification performance, with a silhouette value of 0.184, highlighting the discriminatory power of dynamic behavioral traits. Furthermore, segmentation results disclose varying dominant features across income brackets: demographic factors are primary for low-income groups, behavioral metrics dominate middle-income segments, while social network indicators are key for high-income populations. These insights confirm PSO-KM’s potential in refining digital profiling processes and fostering the advancement of grassroots digital governance practices.