Search research articles

Search research articles

Related Experiment Videos

Measuring the similarity between trajectories using clustering techniques.

Ben H. Jansen¹, Henrik N. Nyberg

¹Department of Electrical Engineering and Bioengineering Research Center, University of Houston, Houston, Texas 77204-4793.

Chaos (Woodbury, N.Y.)

|April 1, 1993

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026

Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026

Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026

Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026

Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026

Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

A new clustering method groups signals by dynamic behavior using trajectory geometry. This approach reliably identifies similar signal patterns in both simulated stochastic and nonlinear data.

Area of Science:

Dynamical Systems Analysis
Time Series Analysis
Computational Statistics

Background:

Analyzing dynamic behavior in time series data is crucial for understanding complex systems.
Existing clustering methods may not effectively capture the geometric properties of signal trajectories.

Purpose of the Study:

To develop and validate a novel clustering method for grouping signals based on similar dynamic behaviors.
To assess the efficacy of trajectory geometry features in hierarchical clustering.

Main Methods:

Time delay embedding to construct state-space trajectories from time series.
Definition and statistical testing of trajectory geometric features.
Application of hierarchical clustering analysis to trajectory features.

Related Experiment Videos

Main Results:

The developed trajectory-based clustering algorithm successfully grouped simulated stochastic (AR model) and nonlinear (Duffing oscillator) data.
Geometric features proved useful for identifying groups of similar trajectories.
The algorithm demonstrated reliable performance across different data types.

Conclusions:

The trajectory-based clustering method provides a robust approach for analyzing signal dynamics.
This method enhances the ability to identify and group signals with similar dynamic characteristics.
The approach is effective for both linear stochastic and nonlinear systems.