Classify Glassy Antiques Based on the Chemical Components
DOI:
https://doi.org/10.54097/hbem.v10i.8118Keywords:
K-means clustering; particle swarm optimization algorithm; glassy antique classification.Abstract
As one sort of cultural relic, glassy antiques can effectively convey the historical information of a certain era and reveal the cultural exchanges in different regions as a symbol of foreign trade. However, due to long-term weathering and corrosion when buried in soil, the shape, color, and chemical components of a glassy antique can change considerably, and hence identifying it and recognizing its category is particularly difficult. Clustering is a popular technique of data analysis and data mining. K-means is one of the most popular data mining algorithms, as it is simple, scalable, and easy to modify in different contexts and fields of application. This paper uses k-means to find the clustering center showing the characteristics of the chemical components of different categories of glassy antiques. Then the particle swarm optimization algorithm (PSO) that offers a globalized detailed search methodology is utilized to improve the k-means clustering. The new result compared to the previous one of traditional k-means clusterin shows better classification capacity. Finally, it compares the results of k-means with that of PSO-k-means and analysis the advantages and disadvantages of PSO-k-means.
Downloads
References
Gan, FX. The Road of Glass and Jade -- Also on the cultural and technical exchanges between China and foreign countries on the silicate cultural relics of the pre-Qin Dynasty (English) [J]. Journal of Silicate, 2013,41 (04): 458-466
Li, M. Preparation of Feons products and research on lead barium glass during the Warring States, Qin and Han Dynasties [D]. Beijing University of Chemical Technology, 2014
Liu, Z., Di, X., Wang, Q., & Wang, L. (2022). Composition analysis of glass relics based on a clustering model. Highlights in Science, Engineering and Technology, 21, 246–253.
Yin, H. (2022). Identification model of surface composition of glass relics based on K-means clustering and boosting learning strategy. Highlights in Science, Engineering and Technology, 22, 340–346.
Fan Yixuan, Kan Xiu, Cao Le, Shen Jie. Application of improved PSO-K-means algorithm in vehicle driving cycle estimation [J]. Intelligent Computer and Application, 2021,11 (07): 80-85+90
Jin, J., Zhong, M., Lin, X., Tian, CH. A New Cluster Analysis Based on Combinatorial Particle Swarm Optimization Algorithm. Proceedings of the 2016 International Conference on Education, Management, Computer and Society. Advances in Computer Science Research, 2016, 476-479
Taher Niknam, Babak Amiri, an efficient hybrid approach based on PSO, ACO and k-means for cluster analysis, Applied Soft Computing, Volume 10, Issue 1,2010, Pages 183-197
Liu Song, Lv Liangbo, Li Qinghui, Xiong Zhaoming. Glass beads unearthed from Lingnan Han Tombs and Sino-foreign exchanges on the Maritime Silk Road of the Han Dynasty [J]. Cultural Relics Protection and Archaeological Sciences, 2019, 31 (04): 18-29.
Shakhatreh M, Shakhatreh H, Ababneh A. Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks[J]. Mobile Information Systems, 2023, 2023.
Sadeghi M, Dehkordi M N, Barekatain B, et al. Correction to: Improve customer churn prediction through the proposed PCA PSO K means algorithm in the communication industry[J]. The Journal of Supercomputing, 2023: 1-1.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
