Clustering Brownian motion trajectories using machine learning


  • Kirichenko Lyudmila
  • Khatsko Daryna
  • Zinchenko Petro



Brownian motion with drift, motion of trapped particles, machine learning clustering, DBSCAN


The article is dedicated to detecting traps encountered by a Brownian particle based on machine learning methods. The trajectory of the Brownian particle was modeled using a drift-extended Brownian motion model, encompassing both free dif-fusion and particle movement within a trap. The density-based spatial clustering of applications with noise (DBSCAN) method was employed for clustering the motion trajectory. The versatility of this method allows the identification of clusters without prior knowledge of their quantity or shape, making it suitable for trap detection. The conducted research demonstrates that the application of the DBSCAN method achieves an average accuracy of 95.0%


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