DBSCAN (Density Based Clustering of Applications with Noise): Difference between revisions

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{{MHRA
{{MHRA
|Publication Year=2022
|Publication Year=1996
|Access=Open
|Access=Open
|Link=https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page---------------------------
|Link=https://cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page
|Organisation(s)/Authors=Institute for Computer Science, University of Munich / Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu
|Author(s)=Ester, M., Kriegel, H.P., Sander, J., and Xu, X.
|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
|Organisation(s)=University of Munich
|Description=Clustering algorithms are used for class identification in spatial databases. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is designed to identify clusters with arbitrary shapes, specifically for large datasets. It relies on a "density-based notion of clusters which is designed to discover clusters of arbitrary shape" (Ester et al., 1996). 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
|Technical Considerations=R package(s): https://cran.r-project.org/web/packages/dbscan/index.html


Latest revision as of 15:52, 4 April 2025

Publication Year: 1996

Access: Open

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

Author(s): Ester, M., Kriegel, H.P., Sander, J., and Xu, X.

Organisation(s)/Authors: University of Munich

Description:

Clustering algorithms are used for class identification in spatial databases. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is designed to identify clusters with arbitrary shapes, specifically for large datasets. It relies on a "density-based notion of clusters which is designed to discover clusters of arbitrary shape" (Ester et al., 1996). 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

Key Words:

spatiotemporal clustering, multi-hazard modeling, hazard footprints

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