ASA136 is a C++ library which divides M points in N dimensions into K clusters so that the within-clusters sum of squares is minimized.
ASA136 is Applied Statistics Algorithm 136. Source code for many Applied Statistics Algorithms is available through STATLIB.
In the K-Means problem, a set of N points X(I) in M-dimensions is given. The goal is to arrange these points into K clusters, with each cluster having a representative point Z(J), usually chosen as the centroid of the points in the cluster. The energy of each cluster is
E(J) = Sum ( all points X(I) in cluster J ) || X(I) - Z(J) ||^2
For a given set of clusters, the total energy is then simply the sum of the cluster energies E(J). The goal is to choose the clusters in such a way that the total energy is minimized. Usually, a point X(I) goes into the cluster with the closest representative point Z(J). So to define the clusters, it's enough simply to specify the locations of the cluster representatives.
This is actually a fairly hard problem. Most algorithms do reasonably well, but cannot guarantee that the best solution has been found. It is very common for algorithms to get stuck at a solution which is merely a "local minimum". For such a local minimum, every slight rearrangement of the solution makes the energy go up; however a major rearrangement would result in a big drop in energy.
A simple algorithm for the problem is known as "H-Means". It alternates between two procedures:
A more sophisticated algorithm, known as "K-Means", takes advantage of the fact that it is possible to quickly determine the decrease in energy caused by moving a point from its current cluster to another. It repeats the following procedure:
The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.
ASA136 is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version.
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SPAETH2, a dataset directory which contains test data for clustering.
You can go up one level to the C++ source codes.