Hierarchical Clustering is based on the general concept of finding a hierarchy of partial clusters, built using either a bottom-up or a top-down approach. More formally, they are split into two categories:
- Agglomerative Clustering: The process starts from the bottom (each initial cluster is made up of a single element) and proceeds by merging the clusters until a stop criterion is reached. In general, the target has a sufficiently small number of clusters at the end of the process.
- Divisive Clustering: In this case, the initial state is a single cluster with all samples, and the process proceeds by splitting the intermediate cluster until all the elements are separated. At this point, the process continues with an aggregation criterion based on dissimilarity between elements. A famous approach is called Divisive Analysis (DIANA); however, that algorithm...
