matlab_kmeans_test, a MATLAB code which illustrates how MATLAB's kmeans() command can be used to handle the K-Means problem, which organizes a set of N points in M dimensions into K clusters.
Because kmeans() is a built-in function in MATLAB, you can examine its source code by starting MATLAB and then typing
edit kmeans
The information on this web page is distributed under the MIT license.
matlab_kmeans_test is available in a MATLAB version.
asa058, a MATLAB code which implements the K-means algorithm of Sparks.
asa136, a MATLAB code which implements the Hartigan and Wong clustering algorithm.
cities, a MATLAB code which handles various problems associated with a set of "cities" on a map.
cities, a dataset directory which contains sets of data defining groups of cities.
image_quantization, a MATLAB code which demonstrates how the KMEANS algorithm can be used to reduce the number of colors or shades of gray in an image.
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_cluster_test, a MATLAB code which takes a set of N points on a trajectory of solutions to the Lorenz equations, and applies the K-means algorithm to organize the data into K clusters.
sammon_data, a MATLAB code which generates six sets of M-dimensional data for cluster analysis.
spaeth, a dataset directory which contains a set of test data.
spaeth2, a dataset directory which contains a set of test data.
There are data files read by the sample code: