Cluster Analysis as a First Step in the 
                     Knowledge Discovery Process.

                          Andreas Rauber,
               Department of Software Technology, 
                Vienna University of Technology,
                    Favoritenstr. 9 - 11 /188, 
                     A--1040 Vienna, Austria,
                   www.ifs.tuwien.ac.at/~andi.
		   
		            Jan Paralic,
        Department of Cybernetics and Artificial Intelligence
	           Technical University of Kosice
                            Letna 9
              SK - 04200 Kosice, Slovak Republic
                       www.tuke.sk/~paralic

Abstract:
Cluster Analysis is one of the most prominent methods for the analysis 
of large, unknown data sets. It provides a particularly suitable tool 
for obtaining a first overview of the data, forming a prominent 
starting point for further evaluation.

In this paper we present some lessons learned during the application 
of two clustering approaches to the analysis of castle admission tickets 
sales data.
A Bayesian unsupervised classification based on AutoClass, and an 
unsupervised neural network, the Self-Organizing Map, are used to 
obtain a first impression of the available data to form the basis 
for further exploration.
We show that this type of cluster analysis provides a suitable first 
step in the knowledge discovery process. The different types of result representation and their suitability of providing a first insight into 
the data set are analyzed and compared.

Keywords: Data Mining, Cluster Analysis, Bayesian Classifier, 
          Neural Networks.
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In:  Journal of Advanced Computational Intelligence (JACI), Vol. 4, No., 4, December 2000.