Final answer:
Hierarchical Clustering can be categorized into two types: Agglomerative, which is a bottom-up approach starting with each observation as a separate cluster and combining clusters, and Divisive, a top-down approach starting with all observations in one cluster and dividing them.
Step-by-step explanation:
There are two types of Hierarchical Clustering: Agglomerative and Divisive. Agglomerative clustering is a "bottom-up" approach where each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
On the other hand, Divisive clustering is a "top-down" approach where all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. The choice between agglomerative and divisive methods depends on the data and the desired granularity of analysis.
Agglomerative clustering is more common in various applications due to its simplicity and ease of implementation. Clusters created using this method can be visualized using a dendrogram, which displays the sequence of cluster merge or split actions.
Meanwhile, Divisive clustering is less common but can be more suitable for specific applications where one needs to identify a strong division in data first.