  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 ($\leq 100$ 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.
