Machine Learning Algorithms

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 called:

• 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 elements are separated. At this point, the process continues with an aggregation criterion ...