To achieve scientific progress in terms of building a cumulative body of knowledge, careful
attention to benchmarking is of the utmost importance, requiring that proposals of new …

Method comparisons are essential to provide recommendations and guidance for applied
researchers, who often have to choose from a plethora of available approaches. While many …

Comparative simulation studies are workhorse tools for benchmarking statistical methods.
As with other empirical studies, the success of simulation studies hinges on the quality of …

We warn against a common but incomplete understanding of empirical research in machine
learning that leads to non-replicable results, makes findings unreliable, and threatens to …

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 …

The constant development of new data analysis methods in many fields of research is
accompanied by an increasing awareness that these new methods often perform better in …

This study examines the multifaceted field of injuries and their impacts on performance in the
National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining …

Mining data containing density-based clusters is well-established and widespread but faces
problems when it comes to systematic and reproducible comparison and evaluation …

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for
current clustering methods. We introduce SHADE (Structure-preserving High-dimensional …

In an era of Biology where modern imaging and sequencing technologies allow to study
almost any biological process at molecular levels, it is now possible to study the content …