In an AHC analysis, we begin by assuming every object belongs to its own individual cluster (i.e., if there are 𝑛 objects in a set, we begin with 𝑛 clusters all of size 1). In each step, the least dissimilar (or most similar) clusters-which will initially be the least dissimilar individual objects-are merged to create a new cluster. This procedure continues until all objects in the set belong to a single cluster; the sequence by which clusters are merged and the dissimilarities between merged clusters are tracked throughout the process. An excellent overview of AHC algorithms and specific implementation considerations can be found in .