Pierre Didier

Laurent Bonnefille

K-means clustering splits a set of objects into a selected number of group.

**How to use the applet :**

- press "clear" to erase the data points

- press "run" to run the algorithm and then press "next step" to see each step

- press "unlock" to add or drag points while the algorithm is running

- press "reset" to get random points

- first scroll bar : to change the number of points

- second scroll bar : to change the number of classes
- left mouse button : to add points

- riht mouse button : to delete points

**K-means algorithm :**

- choose NC random points (NC : number of classes)

- find the closest points to each of those points to get classes

loop

- compute the mean of each class

- find the new classes (the closest points) considering the mean as the center of the class

- if no point has changed of class, the algorithm is finished