Hierarchical clustering is an unsupervised learning method that divides data into groups based on similarity measurements, known as clusters, to construct a hierarchy; this clustering is divided into Agglomerative and Divisive clustering, with Agglomerative clustering being the first.
Agglomerative The hierarchical clustering method allows clusters to be read from bottom to top, and the algorithm always reads from the sub-component first before moving to the parent. Divisive, on the other hand, employs a top-down method in which the parent is visited first, followed by the child.
The divisive clustering algorithm is a top-down clustering approach in which all points in the dataset are initially assigned to one cluster and then split iteratively as one progresses down the hierarchy.
One disadvantage is that groups with close pairs may combine sooner than is ideal, even if they are dissimilar overall. Complete Linkage: estimates the similarity of the two pairs that are the farthest apart. Outliers might induce less-than-optimal merging, which is one downside of this strategy.
The most frequent type of hierarchical clustering used to put objects in clusters based on their similarity is agglomerative clustering. AGNES is another name for it (Agglomerative Nesting). Each item is first treated as a singleton cluster by the algorithm.