  This paper introduces the equiwide clustering problem, where valid partitions
must satisfy intra-cluster dissimilarity constraints. Unlike most existing
clustering algorithms, equiwide clustering relies neither on density nor on a
predefined number of expected classes, but on a dissimilarity threshold. Its
main goal is to ensure an upper bound on the error induced by ultimately
replacing any object with its cluster representative. Under this constraint, we
then primarily focus on minimizing the number of clusters, along with potential
sub-objectives. We argue that equiwide clustering is a sound clustering
problem, and discuss its relationship with other optimization problems,
existing and novel implementations as well as approximation strategies. We
review and evaluate suitable clustering algorithms to identify trade-offs
between the various practical solutions for this clustering problem.
