KMEANS_FAST is a MATLAB library which handles the K-Means problem, which organizes a set of N points in M dimensions into K clusters, by Charles Elkan.
KMEANS_FAST is available in a MATLAB version.
ASA058, a MATLAB library which implements the K-means algorithm of Sparks.
ASA136, a MATLAB library which implements the Hartigan and Wong clustering algorithm.
CITIES, a MATLAB library 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 library 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 library which contains several different algorithms for the K-Means problem, which organizes a set of N points in M dimensions into K clusters;
LORENZ_CLUSTER, a MATLAB library 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.
MATLAB_KMEANS, MATLAB programs which illustrate the use of MATLAB's kmeans() function for clustering N sets of M-dimensional data into K clusters.
SAMMON_DATA, a MATLAB program 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.
You can go up one level to the MATLAB source codes.