  Typing methods are widely used in the surveillance of infectious diseases,
outbreaks investigation and studies of the natural history of an infection. And
their use is becoming standard, in particular with the introduction of High
Throughput Sequencing (HTS). On the other hand, the data being generated is
massive and many algorithms have been proposed for phylogenetic analysis of
typing data, addressing both correctness and scalability issues. Most of the
distance-based algorithms for inferring phylogenetic trees follow the
closest-pair joining scheme. This is one of the approaches used in hierarchical
clustering. And although phylogenetic inference algorithms may seem rather
different, the main difference among them resides on how one defines cluster
proximity and on which optimization criterion is used. Both cluster proximity
and optimization criteria rely often on a model of evolution. In this work we
review, and we provide an unified view of these algorithms. This is an
important step not only to better understand such algorithms, but also to
identify possible computational bottlenecks and improvements, important to deal
with large data sets.
