Paper:
Language: English:

Cluster Analysis as a First Step in the Knowledge Discovery Process

Andreas Rauber^* and Jan Paralic^**

*Department of Software Technology, Vienna University of Technology Favoritenstr., 9 - 11 / 188, A - 1040 Vienna, Austria
**Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Letna 9, 04200 Kosice, Slovak Republic

Received: May 12, 2000

Accepted: July 20, 2000

Keywords: Data mining, Cluster analysis, Bayesian classifier, Neural networks

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.4, No.4 pp. 258-262, 2000

Abstract

Cluster analysis is one of the most prominent methods for the analysis of large, unknown datasets. It provides a particularly suitable tool for obtaining a first overview of 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 ticket 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 datasets are analyzed and compared.

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