Peer groups for organisational learning: Clustering with practical constraints

Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with business constraints such as size and stability considera...

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Bibliographic Details
Published in:PloS one 2021-06, Vol.16 (6), p.e0251723
Main Authors: Kennedy, Daniel W, Cameron, Jessica, Wu, Paul P. -Y, Mengersen, Kerrie
Format: Article
Language:English
Subjects:
Biology and Life Sciences
Cluster analysis
Clustering
Colleges & universities
Editing
Engineering
Local government
Mathematical analysis
Methodology
Methods
Organizational learning
Peers
People and Places
Physical Sciences
Research and Analysis Methods
Reviews
Social Sciences
Statistics
Technology
Visualization
Citations: Items that this one cites
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Summary:Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with business constraints such as size and stability considerations. Additionally, statistical peer groups are constructed from many different variables, and can be difficult to understand, especially for non-statistical audiences. We developed methodology to apply business constraints to clustering solutions and allow the decision-maker to choose the balance between statistical goodness-of-fit and conformity to business constraints. Several tools were utilised to identify complex distinguishing features in peer groups, and a number of visualisations are developed to explain high-dimensional clusters for non-statistical audiences. In a case study where peer group size was required to be small ([less than or equal to] 100 members), we applied constrained clustering to a noisy high-dimensional data-set over two subsequent years, ensuring that the clusters were sufficiently stable between years. Our approach not only satisfied clustering constraints on the test data, but maintained an almost monotonic negative relationship between goodness-of-fit and stability between subsequent years. We demonstrated in the context of the case study how distinguishing features between clusters can be communicated clearly to different stakeholders with substantial and limited statistical knowledge.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0251723