  Note: A revised version of this is now published. Please cite and read (it's
open access): Van Mechelen, I., Boulesteix, A.-L., Dangl, R., Dean, N., Hennig,
C., Leisch, F., Steinley, D., Warrens, M. J. (2023). A white paper on good
research practices in benchmarking: The case of cluster analysis. WIREs Data
Mining and Knowledge Discovery, e1511. https://doi.org/10.1002/widm.1511
  To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made.
