Study of Ancient Glass Classification and Subclassification Based on Systematic Clustering Models

Authors

  • Jun Luo
  • Peiyuan Jiao
  • Keyan Zeng
  • Yixin Zhang

DOI:

https://doi.org/10.54097/ajst.v7i1.10984

Keywords:

Systematic clustering, Hard voting, Glass classification.

Abstract

Ancient glass is highly susceptible to weathering by environmental influences, resulting in changes to its internal chemical composition, which can affect the correct determination of its category. For the purpose of classifying ancient glass, three indicators of classification were chosen based on the magnitude of the mean value of each chemical composition of the two main types of glass. The ???? principle and the lower quartile principle are chosen, respectively, to determine the critical values according to whether the data follow a normal distribution or not, and the final classification results are obtained by hard voting. The hard voting model was trained with an accuracy of 97%. The two main classes of glass were then subclassed according to systematic class clustering, and by making the real data fluctuate within ±1%, the clustering was performed again under the same conditions. The same results were found as for the stable data clustering, indicating that the systematic class clustering model is stable for subclassification of each glass category.

Downloads

Download data is not yet available.

References

Ahmad Zahoor,Nguyen Tuan-Khai,Rai Akhand,Kim Jong-Myon. Industrial fluid pipeline leak detection and localization based on a multiscale Mann-Whitney test and acoustic emission event tracking[J]. Mechanical Systems and Signal Processing,2023,189.

Wall Emerson Robert. Mann-Whitney U test and t-test[J]. Journal of Visual Impairment & Blindness,2023,117(1).

Yang J,Wang Z,Gao S. Component Analysis and Classification Model of Ancient Glassware Based on K-means Clustering and Random Forest[C]//Wuhan Zhicheng Times Cultural Development Co., Ltd..Proceedings of 2023 International Conference on Mathematical Modeling, Algorithm and Computer Simulation (MMACS 2023).2023:398-406.

Zhilin Huang,Yali Xiong,Yifeng Fu,Feng Li. A study of the problem of compositional analysis of ancient glassware based on grey models[J]. Academic Journal of Materials & Chemistry, 2023, 4(2).

Xu K,Chen Y,Nie J. Subclass classification of ancient glassware based on K-Means and GMM[C]//Wuhan Zhicheng Times Cultural Development Co., Ltd..Proceedings of 2023 International Conference on Mathematical Modeling, Algorithm and Computer Simulation (MMACS 2023). 2023:278-285.

Dong K. Composition analysis and identification of ancient glass based on K-Means clustering[C]//Wuhan Zhicheng Times Cultural Development Co., Ltd..Proceedings of 2023 International Conference on Mathematical Modeling, Algorithm and Computer Simulation (MMACS 2023). 2023: 347-356.

Du Y,Zhou J,Li J. Classification and Identification of Ancient Glass Objects based on K-means++ and Fisher Discriminant [C]//Wuhan Zhicheng Times Cultural Development Co., Ltd..Proceedings of 2023 International Conference on Mathematical Modeling, Algorithm and Computer Simulation (MMACS 2023).2023:357-365.

Lu Jiajia. Ancient glass classification model based on integrated feature selection and random forest[J]. Journal of Silicates, 2023,51(04):1060-1065.

McIntyre Amanda,Saikaley Marcus,Janzen Shannon,Blackport Daymon,Singh Jaswinder,Baker Carmen,Miller Tom,Teasell Robert. A Random Forest Classification Model for Spasticity after Stroke[J]. Archives of Physical Medicine and Rehabilitation,2022,103(12).

ChávezDurán Álvaro Agustín, OlveraVargas Miguel, Figueroa Rangel Blanca, García Mariano, Aguado Inmaculada, RuizCorral José Ariel. Mapping Homogeneous Response Areas for Forest Fuel Management Using Geospatial Data, K-Means, and Random Forest Classification[J]. Forests, 2022, 13(12).

Downloads

Published

11-08-2023

Issue

Section

Articles

How to Cite

Study of Ancient Glass Classification and Subclassification Based on Systematic Clustering Models. (2023). Academic Journal of Science and Technology, 7(1), 31-37. https://doi.org/10.54097/ajst.v7i1.10984

Similar Articles

1-10 of 108

You may also start an advanced similarity search for this article.