Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Association Rule Mining in Urban Spatial Data Mining in Digital Twin Modeling
In order to solve the problems of multi-source heterogeneity, inconsistent data formats, uneven data quality, and low efficiency in analysis and mining of urban spatial data (SD), association rule mining technology was introduced to establish a framework for urban SD mining, and an empirical analysis was conducted using S city as an example. The results showed that using association rule mining technology can improve the accuracy (Max: 95%) and integrity (Max: 95.4%) of urban SD, and the established urban SD mining framework can improve the efficiency of SD mining (Max: 89.5%). This study has provided a reference for the fusion application of multi-source heterogeneous data, new methods and ideas for digital twin modeling, and reference for the application of urban SD mining technology in urban governance.
Association Rule Mining in Urban Spatial Data Mining in Digital Twin Modeling
In order to solve the problems of multi-source heterogeneity, inconsistent data formats, uneven data quality, and low efficiency in analysis and mining of urban spatial data (SD), association rule mining technology was introduced to establish a framework for urban SD mining, and an empirical analysis was conducted using S city as an example. The results showed that using association rule mining technology can improve the accuracy (Max: 95%) and integrity (Max: 95.4%) of urban SD, and the established urban SD mining framework can improve the efficiency of SD mining (Max: 89.5%). This study has provided a reference for the fusion application of multi-source heterogeneous data, new methods and ideas for digital twin modeling, and reference for the application of urban SD mining technology in urban governance.
Association Rule Mining in Urban Spatial Data Mining in Digital Twin Modeling
Liu, Weitong (Autor:in) / Wen, Zhicheng (Autor:in) / Li, Xiaoyu (Autor:in) / Wang, Ping (Autor:in)
04.03.2024
344613 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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