Online Clustering Algorithms for Semantic-Rich Network Trajectories

Online algorithms for clustering problems

Assessment of the Performance of Clustering Algorithms in the Extraction of Similar Trajectories

In recent years, the tremendous and increasing growth of spatial trajectory data and the necessity of processing and extraction of useful information and meaningful patterns have led to the fact that many researchers have been attracted to the field of spatio-temporal trajectory clustering. The process and analysis of these trajectories have resulted in the extraction of useful information whic...

Clustering Algorithm for Network Constraint Trajectories

Spatial data mining is an active topic in spatial databases. This paper proposes a new clustering method for moving object trajectories databases. It applies specifically to trajectories that only lie on a predefined network. The proposed algorithm (NETSCAN) is inspired from the wellknown density based algorithms. However, it takes advantage of the network constraint to estimate the object dens...

Modularity-based Clustering for Network-constrained Trajectories

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 cl...

Online Clustering Algorithms

We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the ...