Glass subclass classification model based on systematic cluster analysis and fisher discriminant analysis

Renjie Li; Zhuoyi Yao; Qunxi Zhu; Dongru Han

doi:10.1117/12.2686026

28 July 2023 Glass subclass classification model based on systematic cluster analysis and fisher discriminant analysis

Renjie Li, Zhuoyi Yao, Qunxi Zhu, Dongru Han

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127560G (2023) https://doi.org/10.1117/12.2686026
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

The environment in which ancient glass is buried can easily affect it and cause it to weather. During the weathering process, an extensive exchange of internal elements with environmental elements can change the composition ratio, thus affecting the correct determination of its category. Therefore, studying surface weathering characteristics and classifying glass artifacts is very important in archaeology. For the existing batch of relevant data (surface weathering, color, decoration, type, and chemical composition) of ancient glass artifacts in China, we first did the pre-processing work of relevant data, removing the outliers and adding the missing values for subsequent use. Then we used R-type and Q-type systematic cluster analysis to construct a subclass classification model based on systematic cluster analysis to obtain the appropriate chemical composition and use it as an indicator to classify glass artifacts into subclasses. Then we used the fisher discriminant analysis method to construct a fisher discriminant analysis-based glass-type identification model. With the help of the above model, we identified the type of glass artifacts belonging to unknown categories.

Citation Download Citation

Renjie Li, Zhuoyi Yao, Qunxi Zhu, and Dongru Han "Glass subclass classification model based on systematic cluster analysis and fisher discriminant analysis", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127560G (28 July 2023); https://doi.org/10.1117/12.2686026

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KEYWORDS

Classification systems

Chemical analysis

Data modeling

Potassium

Chemical composition

Data acquisition

Lead

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