Description: Trajectory clustering is an unsupervised learning method that focuses on grouping trajectories based on their spatial and temporal characteristics. This approach allows for the identification of patterns and similarities in the movements of objects or individuals over time, facilitating the understanding of behaviors and dynamics in various contexts. Trajectories can be represented as sequences of points in space, each associated with a time instant, enabling the analysis of not only location but also direction and speed of movement. The characteristics considered in trajectory clustering include the shape of the trajectory, the distance traveled, the duration of the journey, and the frequency of visits to certain points. This type of analysis is particularly relevant in fields such as geolocation, transportation, and data analysis, where the goal is to optimize resources and improve planning. By grouping similar trajectories, valuable insights can be extracted that help make informed decisions and develop more effective strategies in resource and service management.
