Historical Data Mining and Cultural Heritage Inheritance Path Modeling of Traditional Architecture in the Guangfu Region


  • Jin Ling International College, Krirk University, Bangkok, 10220, Thailand
  • Linhui Hu International College, Krirk University, Bangkok, 10220, Thailand


Data mining, Hierarchical Clustering, Cultural Inheritance, Architectural Model, Path Modelling


Historical data mining plays a crucial role in cultural heritage inheritance by uncovering insights from vast repositories of historical information. Through advanced data analysis techniques, such as machine learning algorithms and pattern recognition, historical data mining enables the extraction of valuable knowledge from historical documents, artifacts, and archaeological findings. in historical data mining for cultural heritage inheritance include data quality and accessibility, cultural sensitivity, and interpretation challenges. Historical datasets may suffer from inconsistencies, incompleteness, or biases, posing challenges to the accuracy and reliability of mining results. Additionally, accessing historical data, especially from remote or protected cultural sites, can be challenging due to legal, logistical, or ethical considerations. Cultural sensitivity is crucial, as historical data may contain sensitive or contentious information that requires careful handling and interpretation to avoid misrepresentation or offense. This study explores the application of historical data mining techniques in the context of cultural heritage inheritance, focusing on traditional architecture in the Guangfu region. Leveraging Software-Defined Hierarchical Clustering Path Modeling (SDHCPM), the research aims to uncover underlying patterns and pathways in the evolution of traditional architecture, shedding light on its historical significance and cultural heritage preservation. By analyzing historical datasets related to architectural styles, construction techniques, and socio-cultural influences, SDHCPM facilitates the construction of a path model that traces the development of traditional architecture over time. Through this approach, the study seeks to enhance our understanding of the cultural heritage of the Guangfu region and provide valuable insights for heritage conservation and revitalization efforts. the average clustering coefficient for traditional architecture in the region is found to be 0.75, indicating a high level of architectural coherence and cultural continuity.


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How to Cite

Ling, J. ., & Hu, L. . (2024). Historical Data Mining and Cultural Heritage Inheritance Path Modeling of Traditional Architecture in the Guangfu Region. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 77–89. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5341



Research Article