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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, 2000Accepted:July 20, 2000Published:July 20, 2000
Keywords:Data mining, Cluster analysis, Bayesian classifier, Neural networks
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.
Cite this article as:A. Rauber and J. Paralic, “Cluster Analysis as a First Step in the Knowledge Discovery Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.4, pp. 258-262, 2000.Data files:
