Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This work addresses clustering for dynamic data, such as aircraft trajectories, with incremental improvements in speed and accuracy.
The paper tackles the problem of clustering batch-sequential data with evolving clusters by developing a novel algorithm based on the dependent Dirichlet process mixture model, which achieves orders of magnitude faster computational time and higher accuracy compared to contemporary methods.
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM, and provides a hard clustering with convergence guarantees similar to those of the k-means algorithm. Empirical results from a synthetic test with moving Gaussian clusters and a test with real ADS-B aircraft trajectory data demonstrate that the algorithm requires orders of magnitude less computational time than contemporary probabilistic and hard clustering algorithms, while providing higher accuracy on the examined datasets.