Glass Classification and Identification Based on K-Means Clustering Model
DOI:
https://doi.org/10.54097/hset.v42i.7057Keywords:
Entropy weighting, K-means clustering model, Spearman's correlation coefficient method, multi-factor variance method.Abstract
This paper proposes a method for the classification and identification of glass artefacts based on a K-mean clustering model. Using weathered and unweathered glass with high potassium and lead-barium as the main constituent as the main samples, a cluster analysis of their chemical composition was carried out to derive subclass subdivision and extract characteristic chemical components, based on which a sub-classification model of unknown glass based on the K-mean clustering model was established to provide a new method for the identification of glass artefact types. The results were analysed for reasonableness and sensitivity, and the model was proved to be of generalisation and application value.
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