EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS
Andreas Rauber, Elias Pampalk, Ján Paralič · University of Zagreb University Computing Centre (SRCE) · 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 ojJer 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.