Open Access
Issue
E3S Web Conf.
Volume 290, 2021
2021 3rd International Conference on Geoscience and Environmental Chemistry (ICGEC 2021)
Article Number 02001
Number of page(s) 9
Section Geological and Hydrological Structure and Environmental Planning
DOI https://doi.org/10.1051/e3sconf/202129002001
Published online 14 July 2021
  1. Hu S, He Z, Wu L, et al. A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data[J]. Computers, Environment and Urban Systems, 2020,80:101442. [Google Scholar]
  2. Luan X, Cheng L, Song Y, et al. Better understanding the choice of travel mode by urban residents: New insights from the catchment areas of rail transit stations[J]. Sustainable Cities and Society, 2020,53:101968. [Google Scholar]
  3. Yue W, Chen Y, Thy P T M, et al. Identifying urban vitality in metropolitan areas of developing countries from a comparative perspective: Ho Chi Minh City versus Shanghai[J]. Sustainable Cities and Society, 2021,65:102609. [Google Scholar]
  4. Koperski K, Han J. Discovery of Spatial Association Rules in Geographic Information Databases[J]. 1995. [Google Scholar]
  5. Bo F. Hybrid spatial data mining methods for site selection of emergency response centers[J]. Natural Hazards, 2014,70(1):643-656. [Google Scholar]
  6. Ma W, Xue C, Zhou J. Mining time-series association rules from Western Pacific spatial-temporal data[J]. IOP Conference Series: Earth and Environmental Science, 2014,17(1):12224-12226. [Google Scholar]
  7. Chi J, Jiao L, Dong T, et al. Quantitative identification and visualization of urban functional area based on POI data[J]. Journal of Geomatics. [Google Scholar]
  8. Sheikh A S, Patidar M R. A Novel Algorithm Dufp for Mining Association Rules in Dynamic Databases[J]. [Google Scholar]
  9. Gang F, Wei Z, Qian Y. An Algorithm of Constrained Spatial Association Rules Based on Binary: International Symposium on Neural Networks: Advances in Neural Networks, 2008[C]. [Google Scholar]
  10. Li X. Mining spatial association rules in spatially heterogeneous environment[J]. International Society for Optics and Photonics, 2008. [Google Scholar]
  11. Agrawal R. Mining Association Rules Between Sets of Items in Large Databases, Sigmod Conference[J]. proc sigmod, 1993. [Google Scholar]
  12. Merdun H, Ozturk Y. arules -A Computational Environment for Mining Association Rules and Frequent Item Sets[J]. Journal of Statistical Software, 2005,014. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.