Clustering model research based on composition analysis and identification of glass products

Lei Lv; Suqing Duan; Liguo Liu; Hao Xu

doi:10.54097/hset.v40i.6508

Authors

Lei Lv
Suqing Duan
Liguo Liu
Hao Xu

DOI:

https://doi.org/10.54097/hset.v40i.6508

Keywords:

Glass component identification, Weighted average prediction, Systematic clustering, K-Means.

Abstract

As we all know, ancient glass buried in the soil will be weathered, and a large number of chemical elements in it will exchange with the chemical elements of the soil. As a result, the proportion of chemical components inside the glass changes greatly, affecting the analysis and identification of ancient glass products. This paper selects the fresh data from weathering samples, establish a clustering model and use SPSS to cluster analysis, and classify the types of each cluster. If there is a significant difference, it shall be divided into subcategories; The K-Means algorithm is used to analyze again, and the observation results are compared with the results without disturbance.

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