In this chapter, we explained what we consider relevant aspects in relation to one of the known techniques in unsupervised learning: cluster analysis.
We began by explaining the need to perform transformations in the data and some techniques to do so, and then we turned to the fundamental aspects of clustering analysis, starting with K-Means and ending with the hierarchical clustering.
Additionally, we provided an alternative for handling qualitative variables in mixed datasets, and some tips for choosing the appropriate algorithm as well as some options for plotting hierarchical clustering.
In the next chapter, we will learn about another grouping technique, the association rules. The association process makes groups of observations and attempts to discover links or associations between different attributes of group. These associations become rules that can, in turn, be used to support future decisions.