ly grouped—or “spherical” and Clusters are
of a similar size. Owing to the nature of the hierarchical clustering algorithm
it may take more time than the k-means method. Both methods make assumptions
and they may work well on some data sets, and fail on others.</p>
<p>Another important factor to consider is that the k-means method always starts
with a random choice of cluster centers, thus it may end in different results
on different runs. That may be problematic when trying to replicate your methods.