Classification and Prediction Model for Ancient Glass Based on CART and Cluster Analysis
This paper focuses on the chemical composition of ancient glass relics and uses CART decision tree,cluster analysis,and principal component analysis methods to conduct a series of analysis and predic-tion.Based on conducting systematic and K-means clustering on different features,the results show that it is the best to classify glass products into two major categories of high potassium and lead barium with three subcategories.Using principal component analysis to analyze chemical components,it was found that when the feature roots were selected as the first six ones,the effect was the best,and the contribution rate of explaining the original variables was relatively large.Based on the above results,a CART decision tree is constructed to predict unknown types of glass products,with high prediction accuracy.
NETL
NSTL
万方数据
维普
