Empirical Evaluation of Clustering Algorithms 

          Andreas Rauber¹, Jan Paralic², Elias Pampalk¹ 

            ¹Vienna University of Technology, 
            Department of Software Technology 
	     {andi, elias}@ifs.tuwien.ac.at 
	     
	    ²Technical University of Kosice, 
   Department of Cybernetics and Artificial Intelligence
	            paralic@tuke.sk 
	     
	     
Abstract: 
Unsupervised data classification can be considered one of the most 
important initial steps in the process of data mining. Numerous 
algorithms have been developed and are being used in this context 
in a variety of application domains. Albeit, only little evidence 
is  available as to which algorithms should be used in which context, 
and which techniques offer promising results when being combined for 
a given task. 
In this paper we present an empirical evaluation of some prominent 
unsupervised data classification techniques with respect to their 
usability and the interpretability of their result representation. 

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In: Proceedings of the 11th International Conference on Information and Intelligent Systems (IIS'2000), September 20. - 22. 2000, Varazdin, Croatia
available at: http://www.ifs.tuwien.ac.at/ifs/research/publications/