  This paper presents a kind of effective synchronization clustering method
based on a linearized version of Vicsek model. This method can be represented
by an Effective Synchronization Clustering algorithm (ESynC), an Improved
version of ESynC algorithm (IESynC), a Shrinking Synchronization Clustering
algorithm based on another linear Vicsek model (SSynC), and an effective
Multi-level Synchronization Clustering algorithm (MSynC). After some analysis
and comparisions, we find that ESynC algorithm based on the Linearized version
of the Vicsek model has better synchronization effect than SynC algorithm based
on an extensive Kuramoto model and a similar synchronization clustering
algorithm based on the original Vicsek model. By simulated experiments of some
artificial data sets, we observe that ESynC algorithm, IESynC algorithm, and
SSynC algorithm can get better synchronization effect although it needs less
iterative times and less time than SynC algorithm. In some simulations, we also
observe that IESynC algorithm and SSynC algorithm can get some improvements in
time cost than ESynC algorithm. At last, it gives some research expectations to
popularize this algorithm.
