Research on intelligent clustering of male upper body. Issue 13 (July 2022)

• Record Type: Journal Article
• Authors: Cheng, Pengpeng; Zeng, Xianyi; Bruniaux, Pascal; Wang, Jianping; Chen, Daoling
• Is Part Of: Textile research journal. Volume 92:Issue 13/14(2022)
• Page Start: 2174
• Page End: 2193
• Publication Date: 2022-07
• Subjects: young men -- intelligent algorithm -- cuckoo search-density peak algorithm -- clustering; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/00405175211000125
• Languages: English
• ISSNs: 0040-5175

Abstract

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.