We have here additional material to the "Levy Walk Clustering"-Paper submitted to the ICDM-PhD-Forum.

1) The "presentation.jar" shows you how the Levy Walk Clustering-algorithm clusters an artificial data set. The
data set is fixed, but the algorithm itself runs every time from anew. Due to the non-deterministic behaviour 
the result will differ every run. This is done in the hope of giving the reader a better understanding of the 
behaviour of Levy Walk Clustering.

2) The code for the algorithm is implemented in Java and can be found in the "source code"-folder. There is an 
independent implementation for 2D and for 3D. I think that here is a good point to stress that I'm a math 
major and this is my first big(ger) programming-job. I apologize for the implementation. 
The source code can be imported and if executed the code will immediately create an artificial data set and
cluster it. The main-method is in the Levy-class. It consists mainly of the two lines
		BenUtil.makeCluster();
		BenUtil.letitrun();
The makeCluster();-command creates the data set. It is situated in the BenUtil-class. There are also the
commands (currently commented) to create the main-data set.
The letitrun();-command activates the algorithm. The public static int plot in the main-method determines how
often the clusering gets plotted. 2 plots every step, 1 just the cluster, 0 plots nothing. At the end of the
algorithm the NMI-value gets calculated and printed to the console.
