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Journal of Information and Organizational Sciences, Vol. 24 No. 2, 2000.

Izvorni znanstveni članak

EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*

Andreas Rauber ; Department of Software Technology, Vienna University of Technology, Vienna, Austria
Elias Pampalk ; Department of Software Technology, Vienna University of Technology, Vienna, Austria
Jan Paralič ; Department of Cybernetics and Artificial intelligence, Technical University of Košice, Košice, Slovakia

Puni tekst: engleski pdf 8.544 Kb

str. 195-209

preuzimanja: 741

citiraj

Sažetak

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.

Ključne riječi

data mining; cluster analysis; hierarchical agglomerative clustering; Bayesian clustering; Self-Organizing Map (SOM); growing hierarchical SOM; generative topographic mapping

Hrčak ID:

78710

URI

https://hrcak.srce.hr/78710

Datum izdavanja:

14.12.2000.

Posjeta: 1.773 *