NSDBSCAN: A DensityBased Clustering Algorithm in Network Space
Abstract
:1. Introduction
2. Basic Idea
3. NSDBSCAN Algorithm
3.1. Generating Density Ordering
3.1.1. Obtaining EpsNeighbors
 (1)
 Setting CDCV of central vertex to 0 and that of other vertices to ∞.
 (2)
 Performing a basic expansion with the central vertex as the start vertex.
 (3)
 Continuing the expansion from the new vertices, which are actually the end vertices of the last expansion.
 (4)
 Repeating step 3 until all expansion paths are blocked. The vertices whose CDCV are neither ∞ nor 0 consist of epsneighbors of the central vertex.
3.1.2. Generating the Density Ordering Table and Graph
3.2. Forming Clusters
4. Experiment
4.1. Dataset
4.2. Preprocessing
 (1)
 Deleting all flyovers and tunnels to make the road network planar.
 (2)
 Extracting the skeletons of divided highways and splitting the road segments where they intersect (Figure 8b).
 (3)
 Moving the point alongside roads to its nearest road segment to create event vertices and establish a correspondence between event vertices and point, followed by creating ordinary vertices where road segments intersect (Figure 8c).
 (4)
 Splitting road segments at event vertices (Figure 8d).
4.3. Results
5. Discussion
5.1. Parameterization
5.2. Oneway and DeadEnd Cases
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Algorithm1: Local Shortest Path Distance Algorithm Input: undirected planar graph N, central vertex cp, radius eps Output: cp’s epsneighbors N_eps(cp)

Algorithm2: Generating Density Ordering Input: undirected planar graph N, radius eps Output: density ordering table, density ordering graph

Algorithm3: Forming Clusters Input: density ordering table, density threshold MinPts Output: densitybased clusters

Clustering Algorithms  Parameters  Cluster Number  silhouette  RS  DB  SD 

NC_DT Algorithm  37  −0.5054  0.2215  0.8167  0.0364  
Heirarchical Algorithm  closestpair distance  10  0.0164  0.5517  0.4878  0.0887 
farthestpair distance  36  0.3949  0.9739  0.9251  0.0264  
averagepair distance  10  0.4466  0.9031  0.7822  0.0866  
medianpair distance  23  0.3420  0.9446  0.8121  0.0393  
radius distance  16  0.4292  0.9426  0.6821  0.0518  
NSDBSCAN Algorithm  eps = 100, MinPts = 10  60  0.4470  0.9968  0.4047  0.0131 
eps = 100, MinPts = 15  38  0.4296  0.9978  0.5004  0.0166  
eps = 100, MinPts = 20  21  0.6740  0.9999  0.3605  0.0180  
eps = 200, MinPts = 15  38  0.2002  0.9719  0.5375  0.0095  
eps = 200, MinPts = 20  31  0.4187  0.9913  0.4407  0.0088  
eps = 200, MinPts = 25  28  0.4703  0.9952  0.4456  0.0113  
eps = 300, MinPts = 20  24  0.2902  0.9628  0.5473  0.0145  
eps = 300, MinPts = 25  23  0.2608  0.9666  0.5213  0.0121  
eps = 300, MinPts = 30  20  0.3220  0.9681  0.4860  0.0119  
eps = 400, MinPts = 25  14  0.3424  0.9307  0.5812  0.0262  
eps = 400, MinPts = 30  16  0.3560  0.9564  0.5610  0.0194  
eps = 400, MinPts = 35  17  0.3777  0.9625  0.5098  0.0158 
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Wang, T.; Ren, C.; Luo, Y.; Tian, J. NSDBSCAN: A DensityBased Clustering Algorithm in Network Space. ISPRS Int. J. GeoInf. 2019, 8, 218. https://doi.org/10.3390/ijgi8050218
Wang T, Ren C, Luo Y, Tian J. NSDBSCAN: A DensityBased Clustering Algorithm in Network Space. ISPRS International Journal of GeoInformation. 2019; 8(5):218. https://doi.org/10.3390/ijgi8050218
Chicago/Turabian StyleWang, Tianfu, Chang Ren, Yun Luo, and Jing Tian. 2019. "NSDBSCAN: A DensityBased Clustering Algorithm in Network Space" ISPRS International Journal of GeoInformation 8, no. 5: 218. https://doi.org/10.3390/ijgi8050218