lorenz_ode_cluster_test


lorenz_ode_cluster_test, a MATLAB code which takes N points on a trajectory of solutions to the Lorenz system of ordinary differential equations (ODE), and applies the K-Means algorithm to organize the data into K clusters.

Licensing:

The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.

Languages:

lorenz_ode_cluster_test is available in a MATLAB version.

Related Data and Programs:

kmeans, a MATLAB code which contains several different algorithms for the K-Means problem, which organizes a set of N points in M dimensions into K clusters;

kmeans_fast, a MATLAB code which contains several different algorithms for the K-Means problem, which organizes a set of N points in M dimensions into K clusters, by Charles Elkan.

lorenz_ode, a MATLAB code which approximates solutions to the Lorenz system, creating output files that can be displayed by Gnuplot.

lorenz_sensitivity_test, a MATLAB code which demonstrates sensitivity to initial conditions in the Lorenz system, using an approach suggested by John D Cook.

matlab_kmeans, MATLAB codes which illustrate the use of MATLAB's kmeans() function for clustering N sets of M-dimensional data into K clusters.

Reference:

  1. Eurika Kaiser, Bernd Noack, Laurent Cordier, Andreas Spohn, Marc Segond, Markus Abel, Guillaume Daviller, Robert Niven,
    Cluster-based reduced-order modelling of a mixing layer,
    Submitted to Journal of Fluid Mechanics.

Source Code:

There are data files read by the sample code:


Last revised on 16 February 2019.