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 lowvariance 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.
For example, REF have utilised dependent Dirichlet process mixture model for the clustering of the segments and a Gaussian process for the modeling of the data within the segments.
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
NIPS
Computer Science Mathematics