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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 MB) str. 195-209 preuzimanja: 273* citiraj
APA 6th Edition
Rauber, A., Pampalk, E. i Paralič, J. (2000). EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*. Journal of Information and Organizational Sciences, 24 (2), 195-209. Preuzeto s https://hrcak.srce.hr/78710
MLA 8th Edition
Rauber, Andreas, et al. "EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*." Journal of Information and Organizational Sciences, vol. 24, br. 2, 2000, str. 195-209. https://hrcak.srce.hr/78710. Citirano 25.04.2019.
Chicago 17th Edition
Rauber, Andreas, Elias Pampalk i Jan Paralič. "EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*." Journal of Information and Organizational Sciences 24, br. 2 (2000): 195-209. https://hrcak.srce.hr/78710
Harvard
Rauber, A., Pampalk, E., i Paralič, J. (2000). 'EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*', Journal of Information and Organizational Sciences, 24(2), str. 195-209. Preuzeto s: https://hrcak.srce.hr/78710 (Datum pristupa: 25.04.2019.)
Vancouver
Rauber A, Pampalk E, Paralič J. EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*. Journal of Information and Organizational Sciences [Internet]. 2000 [pristupljeno 25.04.2019.];24(2):195-209. Dostupno na: https://hrcak.srce.hr/78710
IEEE
A. Rauber, E. Pampalk i J. Paralič, "EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*", Journal of Information and Organizational Sciences, vol.24, br. 2, str. 195-209, 2000. [Online]. Dostupno na: https://hrcak.srce.hr/78710. [Citirano: 25.04.2019.]

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

Posjeta: 469 *