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Wang, C.W. (2022) Study on Typical Day Selection Based on Improved K-means Clustering. MA Thesis, Shanghai Institute of Electrical Engineering, Shanghai.

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• TITLE: Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

  AUTHORS: Yuqiao Zhou, Xinyang Xu, Wenjing Ma

  KEYWORDS: Glass Composition, L1 Regularization Logistic Regression Model, K-Means Clustering Analysis, Elbow Rule, Parameter Verification

  JOURNAL NAME: Applied Mathematics, Vol.15 No.1, January 30, 2024

  ABSTRACT: In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.