An Approach to The Classification of Ancient Glassware Based on K-Means Clustering Models

Jiebin Huang; Jinghao Tang

doi:10.54097/hset.v21i.3146

Authors

Jiebin Huang
Jinghao Tang

DOI:

https://doi.org/10.54097/hset.v21i.3146

Keywords:

K-Means Clustering Model, Subclass Classification, Noise, Sensitivity

Abstract

The study of ancient glassware can be very helpful by building a sound research model. In this paper, we first counted the data onto different glass categories for their chemical composition and quantified the patterns using the composition variability. Then, seven factor sets were selected based on data saturation and a K-Means clustering model were built to complete the classification of their sub-classifications. Finally, a test of the rationality and sensitivity of the model was completed by feeding the clustering model with noise-added glass component data.

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