Abstract
A clustering method has been developed to group signals that display similar dynamic behavior. The procedure involves using the method of time delay embedding to construct a trajectory in state space from a time series. Certain features that characterize the geometry of the trajectory have been defined. These features were subjected to a series of statistical tests to determine their usefulness in a hierarchical clustering analysis. The latter is aimed at finding groups of similar trajectories. The trajectory-based clustering algorithm has been applied to simulated data, which included both stochastic data generated by a linear AR model, and nonlinear data generated by a Duffing oscillator. The results show that the algorithm works reliably in both cases.
References
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Dates
Type | When |
---|---|
Created | 23 years, 1 month ago (July 26, 2002, 8:10 a.m.) |
Deposited | 1 year, 7 months ago (Feb. 1, 2024, 6:02 p.m.) |
Indexed | 1 year, 7 months ago (Feb. 1, 2024, 6:40 p.m.) |
Issued | 32 years, 5 months ago (April 1, 1993) |
Published | 32 years, 5 months ago (April 1, 1993) |
Published Print | 32 years, 5 months ago (April 1, 1993) |
@article{Jansen_1993, title={Measuring the similarity between trajectories using clustering techniques}, volume={3}, ISSN={1089-7682}, url={http://dx.doi.org/10.1063/1.165980}, DOI={10.1063/1.165980}, number={2}, journal={Chaos: An Interdisciplinary Journal of Nonlinear Science}, publisher={AIP Publishing}, author={Jansen, Ben H. and Nyberg, Henrik N.}, year={1993}, month=apr, pages={143–151} }