  Experimental evaluation is a major research methodology for investigating
clustering algorithms and many other machine learning algorithms. For this
purpose, a number of benchmark datasets have been widely used in the literature
and their quality plays a key role on the value of the research work. However,
in most of the existing studies, little attention has been paid to the
properties of the datasets and they are often regarded as black-box problems.
For example, it is common to use datasets intended for classification in
clustering research and assume class la-bels as the ground truth for judging
the quality of cluster-ing. In our work, with the help of advanced
visualization and dimension reduction techniques, we show that this practice
may seriously compromise the research quality and produce misleading results.
We suggest that the applicability of existing benchmark datasets should be
carefully revisited and significant efforts need to be devoted to improving the
current practice of experimental evaluation of clustering algorithms to ensure
an essential match between algorithms and problems.
