Research and Application of System-based Clustering and Principal Component Analysis Algorithms

Abstract

The Silk Road was a channel of cultural exchange between China and the West in ancient times, in which glass was a valuable physical evidence of early trade, and the early glass in China was made by absorbing some foreign technology, which also led to a different chemical composition. Nowadays, most of the glass artifacts are roughly divided into lead-barium glass and high-potassium glass, and each of the different parts of the artifacts will be observed and sampled for analysis in the study of the artifacts, and the identification of their composition types has been hampered by natural weathering over thousands of years. Therefore, in view of such problems and the large number and complexity of chemical components, we propose to sub-classify the different types of glass artifacts through systematic clustering and principal component analysis algorithm model, and the basis of classification is the chemical composition, classification of the content of the different artifact sampling points, that is, the artifact number, and finally through sensitivity analysis to evaluate and test the classification results. The classification results can greatly reduce the workload of analyzing and identifying the types of artifacts, and provide a reference basis and methodological guidance for the problem of identifying and classifying artifacts.