  Subset selection is an important component in evolutionary multiobjective
optimization (EMO) algorithms. Clustering, as a classic method to group similar
data points together, has been used for subset selection in some fields.
However, clustering-based methods have not been evaluated in the context of
subset selection from solution sets obtained by EMO algorithms. In this paper,
we first review some classic clustering algorithms. We also point out that
another popular subset selection method, i.e., inverted generational distance
(IGD)-based subset selection, can be viewed as clustering. Then, we perform a
comprehensive experimental study to evaluate the performance of various
clustering algorithms in different scenarios. Experimental results are analyzed
in detail, and some suggestions about the use of clustering algorithms for
subset selection are derived. Additionally, we demonstrate that decision
maker's preference can be introduced to clustering-based subset selection.
