Study of Ancient Glass Classification and Subclassification Based on Systematic Clustering Models

Abstract

Ancient glass is highly susceptible to weathering by environmental influences, resulting in changes to its internal chemical composition, which can affect the correct determination of its category. For the purpose of classifying ancient glass, three indicators of classification were chosen based on the magnitude of the mean value of each chemical composition of the two main types of glass. The ???? principle and the lower quartile principle are chosen, respectively, to determine the critical values according to whether the data follow a normal distribution or not, and the final classification results are obtained by hard voting. The hard voting model was trained with an accuracy of 97%. The two main classes of glass were then subclassed according to systematic class clustering, and by making the real data fluctuate within ±1%, the clustering was performed again under the same conditions. The same results were found as for the stable data clustering, indicating that the systematic class clustering model is stable for subclassification of each glass category.