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

Van Mechelen, I., Boulesteix, A.‐L., Dangl, R., Dean, N. , Hennig, C., Leisch, F., Steinley, D. and Warrens, M. J. (2023) A white paper on good research practices in benchmarking: the case of cluster analysis. WIREs Data Mining and Knowledge Discovery, 13(6), e1511. (doi: 10.1002/widm.1511)

Abstract

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.

• Item Type: Articles
• Additional Information: The work on this paper has been supported in part by the Research Foundation – Flanders (grants G080219N and K802822N to Iven Van Mechelen), by the Research Fund of KU Leuven (grant C14/19/054, Co-PI: Iven Van Mechelen), by the German Research Foundation (grants BO3139/7–1 and /9-1 to Anne-Laure Boulesteix), by the Bundesministerium für Bildung und Forschung (grant 01IS18036A, Co-PI: Anne-Laure Boulesteix) and by the Engineering and Physical Sciences Research Council (grant EP/K033972/1 to Christian Hennig).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dean, Dr Nema

Creator Roles

Dean, N.: Conceptualization, Methodology, Writing – original draft, Writing – review and editing

• Authors: Van Mechelen, I., Boulesteix, A.‐L., Dangl, R., Dean, N., Hennig, C., Leisch, F., Steinley, D., and Warrens, M. J.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: WIREs Data Mining and Knowledge Discovery
• Publisher: Wiley
• ISSN: 1942-4795
• ISSN (Online): 1942-4795
• Published Online: 26 July 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in WIREs Data Mining and Knowledge Discovery 13(6):e1511
• Publisher Policy: Reproduced under a Creative Commons licence