  Comprehensive benchmarking of clustering algorithms is rendered difficult by
two key factors: (i)~the elusiveness of a unique mathematical definition of
this unsupervised learning approach and (ii)~dependencies between the
generating models or clustering criteria adopted by some clustering algorithms
and indices for internal cluster validation. Consequently, there is no
consensus regarding the best practice for rigorous benchmarking, and whether
this is possible at all outside the context of a given application. Here, we
argue that synthetic datasets must continue to play an important role in the
evaluation of clustering algorithms, but that this necessitates constructing
benchmarks that appropriately cover the diverse set of properties that impact
clustering algorithm performance. Through our framework, HAWKS, we demonstrate
the important role evolutionary algorithms play to support flexible generation
of such benchmarks, allowing simple modification and extension. We illustrate
two possible uses of our framework: (i)~the evolution of benchmark data
consistent with a set of hand-derived properties and (ii)~the generation of
datasets that tease out performance differences between a given pair of
algorithms. Our work has implications for the design of clustering benchmarks
that sufficiently challenge a broad range of algorithms, and for furthering
insight into the strengths and weaknesses of specific approaches.
