% In this example the k-means clustering method is used to cluster a given toy
% data set. In k-means clustering one tries to partition n observations into k
% clusters in which each observation belongs to the cluster with the nearest mean.
% The algorithm class constructor takes the number of clusters and a distance to
% be used as input. The distance used in this example is Euclidean distance.
% After training one can fetch the result of clustering by obtaining the cluster
% centers and their radiuses.

% KMEANS
print KMeans

set_features TRAIN ../data/fm_train_real.dat
set_distance EUCLIDEAN REAL
new_clustering KMEANS

train_clustering 3 1000
radi.txt, centers.txt = get_clustering
