<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Based on the cluster analysis algorithm, this paper collects a large amount of sample data from Huizhou buildings and groups these samples using the cluster analysis algorithm so as to identify different categories of architectural culture types. By dividing the group similarity of each category of buildings, the village cultural characteristics related to each category of buildings are extracted, including information on architectural style, structural features, decorative elements and so on. The results show that the automatic cultural classification feature values are 1.2, 2.5, and 2.9, which highlight the unique characteristics of the Huizhou village culture. Especially in Hongcun and Pai Fang Lane buildings, the cultural features are more significant, with matrix classification degrees of 4.7 and 4.022, respectively, fully highlighting the cultural heritage of these buildings. In addition, the clustering analysis algorithm has only a 9.5% error rate, and the classification accuracies all exceed the high level of 0.9, showing its excellent performance in the extraction of various cultural features.</p>
</abstract>

Based on the cluster analysis algorithm, this paper collects a large amount of sample data from Huizhou buildings and groups these samples using the cluster analysis algorithm so as to identify different categories of architectural culture types. By dividing the group similarity of each category of buildings, the village cultural characteristics related to each category of buildings are extracted, including information on architectural style, structural features, decorative elements and so on. The results show that the automatic cultural classification feature values are 1.2, 2.5, and 2.9, which highlight the unique characteristics of the Huizhou village culture. Especially in Hongcun and Pai Fang Lane buildings, the cultural features are more significant, with matrix classification degrees of 4.7 and 4.022, respectively, fully highlighting the cultural heritage of these buildings. In addition, the clustering analysis algorithm has only a 9.5% error rate, and the classification accuracies all exceed the high level of 0.9, showing its excellent performance in the extraction of various cultural features.

Research on Classification of Huizhou Architectural Culture and Extraction of Cultural Characteristics of Villages Based on Cluster Analysis Algorithm

Anhui University of Finance and Economics

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on the cluster analysis algorithm, this paper collects a large amount of sample data from Huizhou buildings and groups these samples using the cluster...

Research on Classification of Huizhou Architectural Culture and Extraction of Cultural Characteristics of Villages Based on Cluster Analysis Algorithm

Data publikacji: 11 gru 2023

Zakres stron: -

Otrzymano: 26 sty 2023

Przyjęty: 21 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01433

Słowa kluczowe
Huizhou architecture, Cluster analysis, Group similarity, Automatic classification, Error rate

© 2023 You He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Classification of Huizhou Architectural Culture and Extraction of Cultural Characteristics of Villages Based on Cluster Analysis Algorithm

Data publikacji: 11 gru 2023

Zakres stron: -

Otrzymano: 26 sty 2023

Przyjęty: 21 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01433

Słowa kluczoweHuizhou architecture, Cluster analysis, Group similarity, Automatic classification, Error rate

© 2023 You He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Huizhou architecture, Cluster analysis, Group similarity, Automatic classification, Error rate