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>.

翻译：聚类算法的评估通常涉及在多种基准问题上运行这些算法，并将其输出结果与专家提供的参考标准（即真实分组）进行比较。遗憾的是，许多研究论文和研究生论文仅考虑了少量数据集。此外，对同一问题集可能存在多种同样有效的聚类方式这一事实也鲜有考虑。为克服这些局限性，我们开发了一个框架，旨在引入一种用于测试聚类算法的统一方法论。同时，我们收集、整理并标准化了机器学习与数据挖掘文献中常见的多个聚类基准数据集集合，并纳入了不同维度、规模及聚类类型的新数据集。交互式数据集浏览器、Python API文档、与其他编程语言（如R或MATLAB）交互方式的说明，以及其他详细信息均可在<https://clustering-benchmarks.gagolewski.com>获取。