matlab_kmeans_test

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

Licensing:

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

Languages:

matlab_kmeans_test is available in a MATLAB version.

Related Data and Programs:

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.

Reference:

1. John Hartigan, Manchek Wong,
   Algorithm AS 136: A K-Means Clustering Algorithm,
   Applied Statistics,
   Volume 28, Number 1, 1979, pages 100-108.
2. Wendy Martinez, Angel Martinez,
   Computational Statistics Handbook with MATLAB,
   Chapman and Hall / CRC, 2002.
3. David Sparks,
   Algorithm AS 58: Euclidean Cluster Analysis,
   Applied Statistics,
   Volume 22, Number 1, 1973, pages 126-130.

Source code:

• matlab_kmeans_test.m, calls all the tests.
• matlab_kmeans_test.sh, runs all the tests.
• matlab_kmeans_test.txt, the output file.

• matlab_kmeans_test01.m, divides the 75 point data into 4 clusters.
• matlab_kmeans_test02.m, divides the 100 point data into 5 clusters.

There are data files read by the sample code:

• x_m2_n75.txt, 75 points in 2D, with integer coordinates, and 0 < X < 120, 0 < Y < 160, which might naturally be grouped into 4 sets.
• x_m2_n75.png, an image of the points.
• x_m2_n75_k4.png, an image of the points color-coded into 4 clusters.
• x_m2_n100.txt, 100 2D points, used as a case study by the sample problem.
• x_m2_n100.png, an image of the points.
• x_m2_n100_k5.png, an image of the points color-coded into 5 clusters.