Ditton, Elizabeth, Swinbourne, Anne, and Myers, Trina (2022) Selecting a clustering algorithm: A semi-automated hyperparameter tuning framework for effective persona development. Array, 14. 100186.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

When approaching a clustering problem, such as during persona development, selecting the most appropriate algorithm and parameter combination is essential. The hyperparameter tuning process required to determine the best combination is often tedious and thus automated through evaluation metrics. However, there are no ground truth values available for the empirical evaluation of clustering algorithms and existing internal metrics cannot comment on the quality of a set of clusters for their proposed use case. This paper presents a semi-automated framework for the hyperparameter tuning of clustering algorithms for persona development, HyPersona, which minimises the manual intervention required through simple evaluation and the production of informative graphs and early-stage personas. Within HyPersona, an internal metric focused on aspects necessary to developing quality persons, average feature significance (AFS), is proposed to assist in the evaluation of results. HyPersona was validated through application to a real-world persona development problem, evaluating the three most widely used clustering algorithms for persona development. HyPersona was compared to existing hyperparameter tuning and persona development methods and developed personas of a comparable quality whilst reducing manual intervention. The proposed internal metric, AFS, was found to provide a unique insight into the performance of cluster sets for persona development.

Item ID:	74285
Item Type:	Article (Research - C1)
ISSN:	2590-0056
Keywords:	Automated persona development, Machine learning, Clustering algorithms, Hyperparameter tuning, Internal evaluation metrics
Related URLs:	https://researchonline.jcu.edu.au/80696/
Copyright Information:	© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Date Deposited:	31 May 2022 21:44
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461106 Semi- and unsupervised learning @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4608 Human-centred computing > 460806 Human-computer interaction @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4612 Software engineering > 461201 Automated software engineering @ 10%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220407 Human-computer interaction @ 40% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 40% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 20%
Downloads:	Total: 153 Last 12 Months: 8
	More Statistics