  Many real-world systems can be studied in terms of pattern recognition tasks,
so that proper use (and understanding) of machine learning methods in practical
applications becomes essential. While a myriad of classification methods have
been proposed, there is no consensus on which methods are more suitable for a
given dataset. As a consequence, it is important to comprehensively compare
methods in many possible scenarios. In this context, we performed a systematic
comparison of 7 well-known clustering methods available in the R language. In
order to account for the many possible variations of data, we considered
artificial datasets with several tunable properties (number of classes,
separation between classes, etc). In addition, we also evaluated the
sensitivity of the clustering methods with regard to their parameters
configuration. The results revealed that, when considering the default
configurations of the adopted methods, the spectral approach usually
outperformed the other clustering algorithms. We also found that the default
configuration of the adopted implementations was not accurate. In these cases,
a simple approach based on random selection of parameters values proved to be a
good alternative to improve the performance. All in all, the reported approach
provides subsidies guiding the choice of clustering algorithms.
