Permalink
2e2114f Mar 30, 2014
22 lines (16 sloc) 1.2 KB
The following classes can be run without parameters to generate a sample data set and
run the reference clustering implementations over them:
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.
* DisplayCanopy - uses Canopy clustering
* DisplayKMeans - uses k-Means clustering
* DisplayFuzzyKMeans - uses Fuzzy k-Means clustering
* NOTE: 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, violet) 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.
* NOTE: by changing the parameter values (k, ALPHA_0, numIterations) and the display SIGNIFICANCE
you can obtain different results.