🤖 AI Summary
本文通过模式挖掘和无监督学习方法，利用Urban Atlas 2018数据识别欧洲城市中重复出现的土地使用配置，采用Ward聚类法进行分析。
📝 Abstract
Urban areas are intricate systems shaped by socioeconomic, environmental, and infrastructural factors, with land use patterns serving as aspects of urban morphology. This paper proposes a novel methodology leveraging frequent item set mining and unsupervised learning techniques to identify similar cities based on co-occurring land use patterns. The Copernicus program's Urban Atlas data are used as source data. The methodology involves data preprocessing, pattern mining using the negFIN algorithm, postprocessing, and knowledge extraction and visualization. The preprocessing of spatial datasets results in a publicly available transaction dataset. The framework is scalable and the source code is made publicly available.