  In this paper we make two novel contributions to hierarchical clustering.
First, we introduce an anomalous pattern initialisation method for hierarchical
clustering algorithms, called A-Ward, capable of substantially reducing the
time they take to converge. This method generates an initial partition with a
sufficiently large number of clusters. This allows the cluster merging process
to start from this partition rather than from a trivial partition composed
solely of singletons. Our second contribution is an extension of the Ward and
Ward p algorithms to the situation where the feature weight exponent can differ
from the exponent of the Minkowski distance. This new method, called A-Ward
p\b{eta} , is able to generate a much wider variety of clustering solutions. We
also demonstrate that its parameters can be estimated reasonably well by using
a cluster validity index. We perform numerous experiments using data sets with
two types of noise, insertion of noise features and blurring within-cluster
values of some features. These experiments allow us to conclude: (i) our
anomalous pattern initialisation method does indeed reduce the time a
hierarchical clustering algorithm takes to complete, without negatively
impacting its cluster recovery ability; (ii) A-Ward p\b{eta} provides better
cluster recovery than both Ward and Ward p.
