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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.  This article is categorized under:  Fundamental Concepts of Data and Knowledge &gt; Data Concepts  Fundamental Concepts of Data and Knowledge &gt; Key Design Issues in Data Mining  Technologies &gt; Structure Discovery and Clustering

A white paper on good research practices in benchmarking: The case of cluster analysis