Hierarchical clustering algorithms are a good choice when we don't necessarily have circular (or hyperspherical) clusters in the data, and we essentially don't know the number of clusters in advance. With hierarchical clustering algorithm, unlike the flat or partitioning algorithms, there is no requirement to decide and pass the number of clusters to be formed prior to applying the algorithm on the dataset.
Hierarchical clustering results in a dendogram (tree diagram) that can be visually verified to easily determine the number of clusters. Visual verification enables us to perform cuts in the dendrogram at suitable places.
The results produced by this type of clustering algorithm are reproducible as the algorithm is not sensitive to the choice of the distance metric. In other words, irrespective...
