  The evaluation of clustering algorithms can involve running them on a variety
of benchmark problems, and comparing their outputs to the reference,
ground-truth groupings provided by experts. Unfortunately, many research papers
and graduate theses consider only a small number of datasets. Also, the fact
that there can be many equally valid ways to cluster a given problem set is
rarely taken into account. In order to overcome these limitations, we have
developed a framework whose aim is to introduce a consistent methodology for
testing clustering algorithms. Furthermore, we have aggregated, polished, and
standardised many clustering benchmark dataset collections referred to across
the machine learning and data mining literature, and included new datasets of
different dimensionalities, sizes, and cluster types. An interactive datasets
explorer, the documentation of the Python API, a description of the ways to
interact with the framework from other programming languages such as R or
MATLAB, and other details are all provided at
<https://clustering-benchmarks.gagolewski.com>.
