# Introduction¶

Mahout provides examples to visualize sample clusters that gets created by our clustering algorithms. Note that the visualization is done by Swing programs. You have to be in a window system on the same machine you run these, or logged in via a remote desktop.

For visualizing the clusters, you have to execute the Java classes under org.apache.mahout.clustering.display package in mahout-examples module. The easiest way to achieve this is to setup Mahout in your IDE.

# Visualizing clusters¶

The following classes in org.apache.mahout.clustering.display can be run without parameters to generate a sample data set and run the reference clustering implementations over them:

1. DisplayClustering - generates 1000 samples from three, symmetric distributions. This is the same data set that is used by the following clustering programs. It displays the points on a screen and superimposes the model parameters that were used to generate the points. You can edit the generateSamples() method to change the sample points used by these programs.
2. DisplayClustering - displays initial areas of generated points
3. DisplayCanopy - uses Canopy clustering
4. DisplayKMeans - uses k-Means clustering
5. DisplayFuzzyKMeans - uses Fuzzy k-Means clustering
6. DisplaySpectralKMeans - uses Spectral KMeans via map-reduce algorithm

If you are using Eclipse, just right-click on each of the classes mentioned above and choose "Run As -Java Application". To run these directly from the command line:

cd \$MAHOUT_HOME/examples
mvn -q exec:java -Dexec.mainClass=org.apache.mahout.clustering.display.DisplayClustering


You can substitute other names above for DisplayClustering.

Note that some of these programs display the sample points and then superimpose all of the clusters from each iteration. The last iteration's clusters are in bold red and the previous several are colored (orange, yellow, green, blue, magenta) in order after which all earlier clusters are in light grey. This helps to visualize how the clusters converge upon a solution over multiple iterations.