Modularity-Based Clustering for Network-Constrained Trajectories
This addresses trajectory clustering for moving objects on road networks, but it appears incremental as it adapts existing modularity methods to a specific domain.
The paper tackles clustering of network-constrained trajectories by building a similarity graph and using modularity-optimization hierarchical clustering, showing superiority over classic hierarchical clustering in experiments.
We present a novel clustering approach for moving object trajectories that are constrained by an underlying road network. The approach builds a similarity graph based on these trajectories then uses modularity-optimization hiearchical graph clustering to regroup trajectories with similar profiles. Our experimental study shows the superiority of the proposed approach over classic hierarchical clustering and gives a brief insight to visualization of the clustering results.