Machine Learning Algorithms - Second Edition

Hierarchical strategies

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 ...