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JACIII Vol.4 No.4 pp. 258-262
doi: 10.20965/jaciii.2000.p0258
(2000)

Paper:

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
Published:
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:
Andreas Rauber and Jan 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.
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Last updated on Aug. 03, 2021