In this chapter, we discovered hierarchical (or nested) clustering, particularly in its agglomerative form. We used several distance metrics (Euclidean, Manhattan, and binary) as well as several linkage functions. We discussed how to interpret the result of clustering and how cluster analysis can be used for further inquiry of the data, and discussed real-life examples. Another popular application we did not discuss here is text classification. We have also seen that datasets sometimes require some effort (preprocessing) to be made compliant with analytic requirements. In the next chapter, we will see how to use principal component analysis, notably to perform dimensionality reduction.