论文标题
基准测试聚类算法的框架
A framework for benchmarking clustering algorithms
论文作者
论文摘要
聚类算法的评估可能涉及在各种基准问题上运行它们，并将其输出与专家提供的参考，地面实际分组进行比较。不幸的是，许多研究论文和研究生论文仅考虑少数数据集。同样，很少考虑存在许多同样有效的方法来聚集给定问题集的事实。为了克服这些局限性，我们开发了一个框架，其目的是引入一致的方法来测试聚类算法。此外，我们已经在机器学习和数据挖掘文献中汇总，抛光和标准化了许多集群基准数据集集合，其中包括不同维度，尺寸和集群类型的新数据集。一个交互式数据集资源管理器，Python API的文档，描述与其他编程语言（例如R或MATLAB）互动的方式的描述以及其他详细信息，以及其他详细信息，都在<https://clustering-benchmarks.gagolewwski.com>上提供。
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>.
