A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

6 citations as recorded by crossref.

1. Identifying the Structure of Cities by Clustering Using a New Similarity Measure Based on Smart Card Data K. Kim https://doi.org/10.1109/TITS.2019.2910548
2. Reasoning about socio-economic data: a visual analytics approach to Bayesian network E. Chuprikova & L. Meng https://doi.org/10.1080/23729333.2019.1613073
3. Anchor-Enhanced Geographical Entity Representation Learning R. Chen et al. https://doi.org/10.1109/TNNLS.2023.3329822
4. Multi‐graph convolutional clustering network B. Wang et al. https://doi.org/10.1049/sil2.12116
5. Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications G. Li et al. https://doi.org/10.1016/j.trc.2025.105252
6. Integrating Gaussian Mixture Dual-Clustering and DBSCAN for Exploring Heterogeneous Characteristics of Urban Spatial Agglomeration Areas T. Xiao et al. https://doi.org/10.3390/rs14225689