International Journal of Geo-Information Article NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space Tianfu Wang 1, Chang Ren 2 , Yun Luo 1 and Jing Tian 1,3,4,* 1 School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;
[email protected] (T.W.);
[email protected] (Y.L.) 2 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
[email protected] 3 Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 4 Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China * Correspondence:
[email protected]; Tel.: +86-1362-863-6229 Received: 27 March 2019; Accepted: 5 May 2019; Published: 8 May 2019 Abstract: 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.