Study and methodology exploration of high-potassium glass and lead-barium glass classification patterns based on cluster analysis and decision tree

Abstract

This study aims to analyze the classification patterns of high-potassium glass and lead-barium glass. It involves preprocessing the data through cluster analysis and employing machine learning decision trees and K-means clustering models for in-depth analysis. In the clustering analysis model, we start by performing a cluster difference analysis on the feature attributes of high-potassium glass and lead-barium glass to obtain initial classification results. Subsequently, we use machine learning decision tree models to partition the data into training and testing sets to explore the classification patterns. Construct a K-means clustering model by iteratively determining the initial cluster centroids' positions using an algorithm and calculating the silhouette coefficient for different numbers of clusters.This process aids in determining the appropriate number of clusters and the initial cluster centroids' positions. The results indicate significant differences in various features between high-potassium glass and lead-barium glass, and the employment of decision trees and K-means models successfully classifies artifacts. In conclusion, this study provides a robust method for the classification of high-potassium glass and lead-barium glass, offering valuable insights for research and application in related fields.