A framework for benchmarking clustering algorithms

The evaluation of clustering algorithms can involve running them on a variety of benchmark prob-lems, 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.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

The evaluation of clustering algorithms involves running them on benchmark problems and comparing their outputs to expert-provided reference groupings. However, existing research papers and theses often consider only a small number of datasets and fail to account for the multiple valid ways of clustering a given problem set. To address these limitations, researchers have developed a framework that aims to introduce a consistent methodology for testing clustering algorithms and have compiled and standardized various clustering benchmark datasets.