Synchronization Clustering based on a Linearized Version of Vicsek model

TL;DR

This paper introduces several synchronization clustering algorithms based on a linearized Vicsek model, demonstrating improved efficiency and effectiveness over existing methods through simulations.

Contribution

The paper proposes new synchronization clustering algorithms derived from a linearized Vicsek model, showing enhanced performance and reduced computation time.

Findings

ESynC outperforms SynC and original Vicsek-based algorithms in synchronization effect

Algorithms achieve better results with fewer iterations and less computational time

Inequalities in time costs among the proposed algorithms are observed

Abstract

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.