DBSCAN (Density Based Clustering of Applications with Noise)

Publication Year: 2022

Access: Open

Link: https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page---------------------------

Author(s):

Organisation(s)/Authors:

Description:

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. The clustering algorithm DBSCAN relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. Tilloy et al., 2022 has conducted a more recent application of DBSCAN for the evaluation of multi-hazards, which can be found at the following link: https://esd.copernicus.org/articles/13/993/2022/esd-13-993-2022.html

Technical Considerations:

R package(s): https://cran.r-project.org/web/packages/dbscan/index.html python package: https://pypi.org/project/dbscan/

Key Words:

spatiotemporal clustering, multi-hazard modeling, hazard footprints

Year of publication: 2022

Access: Open

Link: https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page---------------------------

Organisation(s) / Author(s): Institute for Computer Science, University of Munich / Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu

Description: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. The clustering algorithm DBSCAN relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. Tilloy et al., 2022 has conducted a more recent application of DBSCAN for the evaluation of multi-hazards, which can be found at the following link: https://esd.copernicus.org/articles/13/993/2022/esd-13-993-2022.html

Technical considerations:

R package(s): https://cran.r-project.org/web/packages/dbscan/index.html

python package: https://pypi.org/project/dbscan/

Keywords:

spatiotemporal clustering, multi-hazard modeling, hazard footprints