The present invention relates to the field of
machine learning, and proposes an ocean
trajectory clustering and predicting method. In order to accurately predict future trajectory points,
trajectory clustering is required first. According to the
trajectory clustering method disclosed by the present invention, similarity measurement is carried out on the trajectory points of complex variability andstrong volatility at sea, and the potential
data information is mined; and the method combines the
Gaussian mixture model GP with the
Dirichlet process DP, and the non-parametric
Bayesian framework of the DP is used to determine the number of clusters to improve cluster adaptability. The
algorithm uses the process of adding Chinese restaurants based on the DP, and uses the collapsed
Gibbs sampling method to solve the model, so that the unsupervised classification from the finite
mixed model to the infinite
mixed model is implemented, the number of clusters can be automatically obtained, and future trajectory points are predicted for the clustered trajectories by using the
Gaussian process regression prediction method. According to the technical scheme of the present invention, the disadvantages of manually specifying the number of clusters and local maximization in parameter
estimation are avoided, and the accuracy of prediction is improved under the premise of ensuring adaptive clustering.