Academic Journal of Materials & Chemistry, 2023, 4(3); doi: 10.25236/AJMC.2023.040306.
Yaowen Zhang1, Jie Guo1, Qiuling Deng2
1Department of Civil Engineering, Guilin University of Technology, Guilin, China
2Department of Electronic Information Engineering, Guilin University of Technology, Guilin, China
In order to study the classification laws of glass types, the data were first divided into two categories of weathered and unweathered points, and then k-means cluster analysis was used to subdivide each category of data into two categories. It was found that the artifacts in these two categories corresponded to high potassium glass and lead-barium glass, respectively, indicating that k-means cluster analysis could be used as a classification law for high potassium glass and lead-barium glass. Since there are 14 chemical components in each of the four categories, it is more difficult and complicated to use them as the basis for subcategory classification, so principal component analysis was applied to reduce the dimensionality, and the 14 chemical components were replaced by comprehensive indicators (principal components) filtered by the cumulative contribution of eigenvalues over 80%. Then, the sample glass was classified into 15 classes by applying SPSS software to classify the principal components of each class as variables, respectively, and the samples as one event for clustering. In order to verify whether the classification method established by this model is realistic, the results of the division of each category into classes were analyzed separately using ROC curves for reasonableness and sensitivity in this paper, and the final reasonableness and sensitivity were both good.
Glass type, Classification law, K-means cluster analysis, Principal component analysis method, Cluster division
Yaowen Zhang, Jie Guo, Qiuling Deng. Identification of ancient glass products based on K-means composition analysis. Academic Journal of Materials & Chemistry (2023) Vol. 4, Issue 3: 39-46. https://doi.org/10.25236/AJMC.2023.040306.
 Wang J-Tao, Zhou L-F, Gao E-S. Sixth lecture on chi-square test [J]. Experimental Animals and Comparative Medicine, 2000(4): 251-254.
 Dong JQ, Li QH, Gan FX, Hu YQ, Cheng YJ, Jiang HJ. Nondestructive analysis of a batch of glassware from Eastern Zhou to Song dynasties excavated in Henan [J]. China Materials Progress, 2012, 31(11): 9-1.
 Pu Huizhong. K-means clustering analysis algorithm in artificial intelligence + personalized learning system [J]. Intelligent Computers and Applications, 2022, 12(08): 152-156.
 Fu Xiufeng, Gan Fuxi. Study on the composition of a group of ancient glass from South and Southwest China based on multivariate statistical analysis [J]. DOI: 10.16334/j.cnki.cn31-1652/k. 2006.04.002.
 Wang Jinghan. ROC curve in clinical medical diagnostic experiments [J]. Chinese Journal of Hypertension, 2008, 16(2): 175-177.