  Capacitated spatial clustering, a type of unsupervised machine learning
method, is often used to tackle problems in compressing, classifying, logistic
optimization and infrastructure optimization. Depending on the application at
hand, a wide set of extensions may be necessary in clustering.
  In this article we propose a number of novel extensions to PACK that is a
novel capacitated spatial clustering method. These extensions are relocation
and location preference of cluster centers, outliers, and non-spatial
attributes. The strength of PACK is that it can consider all of these
extensions jointly. We demonstrate the usefulness PACK with a real world
example in edge computing server placement for a city region with various
different set ups, where we take into consideration outliers, center placement,
and non-spatial attributes. Different setups are evaluated with summary
statistics on spatial proximity and attribute similarity. As a result, the
similarity of the clusters was improved at best by 53%, while simultaneously
the proximity degraded only 18%. In alternate scenarios, both proximity and
similarity were improved. The different extensions proved to provide a valuable
way to include non-spatial information into the cluster analysis, and attain
better overall proximity and similarity. Furthermore, we provide easy-to-use
software tools (rpack) for conducting clustering analyses.
