EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS*Report as inadecuate




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

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.

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



Author: Andreas Rauber - ; Department of Software Technology, Vienna University of Technology, Vienna, Austria Elias Pampalk - ; Departme

Source: http://hrcak.srce.hr/



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