The kmeans algorithm 
This applet demonstrates the kmeans algorithm for a 2d feature space. Kmeans
is one of the simplest unsupervised learning algorithms that partition
feature vectors into k clusters so that the within group sum of squares is minimized.
The procedure follows a simple way to classify a given data set and looks
like that: Step 1: Place ranomly initial group centroids into the 2d space. How to use the applet The area surrounded by the black rectangle represents the 2d feature
space. You can place a feature vector into this space by a mouse click. When
you click the DRAW CLUSTERButton feature vectors will be placed into the
feature space, so that they form gaussian like clusters. The count of the
clusters is determined by the choice list on the top right corner. When you want to start the algorithm press the STARTButton. After
that the STEPButton performs one step of the algorihms. You may be interested
which step the algorithm execute, therefore the current step is emphasized in
the steps framework under the feature space. With the RUNButton the
algorithm runs automatically. If the History Checkbox is enabled you see the
way the centroids move. With the NEW STARTButton you can start the algorithm again in step 1
and with the RESTARTButton the feature vectors will be deleted. The list on
the top right corner sets the number of the clusters k. Downloads: 

Authors: Jens
Spehr and Simon Winkelbach