  Agglomerative hierarchical clustering (AHC) requires only the similarity
between objects to be known. This is attractive when clustering signals of
varying length, such as speech, which are not readily represented in
fixed-dimensional vector space. However, AHC is characterised by $O(N^2)$ space
and time complexity, making it infeasible for partitioning large datasets. This
has recently been addressed by an approach based on the iterative re-clustering
of independent subsets of the larger dataset. We show that, due to its
iterative nature, this procedure can sometimes lead to unchecked growth of
individual subsets, thereby compromising its effectiveness. We propose the
integration of a simple space management strategy into the iterative process,
and show experimentally that this leads to no loss in performance in terms of
F-measure while guaranteeing that a threshold space complexity is not breached.
