Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2013 | neural information processing systems | Conference |
Volume | Citations | PageRank |
abs/1305.6659 | 9 | 0.64 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trevor Campbell | 1 | 38 | 4.94 |
| Miao Liu | 2 | 39 | 6.28 |
| Brian Kulis | 3 | 4700 | 201.68 |
| Jonathan How | 4 | 1759 | 185.09 |
| L. Carin | 5 | 4603 | 339.36 |