Classification and Identification of Ancient Glass Objects based on K-means++ and Fisher Discriminant

Abstract

The significance of this study is to classify and identify the types of ancient glass products according to their chemical composition. The data were selected from the proportion of chemical compositions that have been analyzed for ancient glass, and the best number of clusters (k) for the division was roughly determined using the elbow rule for the five chemical compositions of high potassium and lead barium, respectively, and brought into the k-means++ algorithm for cluster analysis, and then the final determination of k and the evaluation of the rationality of clustering were performed using the contour coefficient, and finally the Fisher discriminant analysis method based on variable meritocracy combined with eigenvalues, Wilke Lambda, and classification function coefficients to identify unknown categories of glass artifacts. The model used was analyzed and evaluated with good results, and the model is applicable to the classification of ancient glass artifacts and identification of the type to which they belong.