NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space

Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clus...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:ISPRS international journal of geo-information Ročník 8; číslo 5; s. 218
Hlavní autoři: Wang, Tianfu, Ren, Chang, Luo, Yun, Tian, Jing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 08.05.2019
Témata:
Algorithms
China
Cluster analysis
Clustering
clustering analysis
Data analysis
Data mining
Datasets
DBSCAN algorithm
Delaunay triangulation
Density
Density distribution
Distribution
Earthquakes
Euclidean geometry
geometry
Neighborhoods
network spatial analysis
quantitative analysis
Spatial analysis
Spatial data
spatial data mining
China
ISSN:2220-9964, 2220-9964
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi8050218