A white paper on good research practices in benchmarking: The case of cluster analysis,WIREs Data Mining and Knowledge Discovery

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A white paper on good research practices in benchmarking: The case of cluster analysis
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2023-07-26 , DOI: 10.1002/widm.1511
Iven Van Mechelen ₁ , Anne‐Laure Boulesteix ₂ , Rainer Dangl ₃ , Nema Dean ₄ , Christian Hennig ₅ , Friedrich Leisch ₃ , Douglas Steinley ₆ , Matthijs J. Warrens ₇

Affiliation

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 methods are extensively and carefully compared with their best predecessors, and existing methods subjected to neutral comparison studies. Answers to benchmarking questions should be evidence-based, with the relevant evidence being collected through well-thought-out procedures, in reproducible and replicable ways. In the present paper, we review good research practices in benchmarking from the perspective of the area of cluster analysis. Discussion is given to the theoretical, conceptual underpinnings of benchmarking based on simulated and empirical data in this context. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made based on existing literature.

中文翻译：

关于基准测试良好研究实践的白皮书：聚类分析案例

为了在积累知识体系方面实现科学进步，认真关注基准测试至关重要，要求新方法的提议与其最好的前辈进行广泛而仔细的比较，并对现有方法进行中立的比较研究。基准测试问题的答案应以证据为基础，通过深思熟虑的程序以可重复和可复制的方式收集相关证据。在本文中，我们从聚类分析领域的角度回顾了基准测试的良好研究实践。在此背景下，讨论了基于模拟和经验数据的基准测试的理论和概念基础。随后，讨论了如何解决聚类中基准测试问题的实用性，并根据现有文献提出了基础性建议。

更新日期：2023-07-26

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