Agglomerative Hierarchical Clustering (AHC) is a straightforward iterative classification approach. Calculating the dissimilarity between the N objects is the first step in the process. The two things or classes of objects whose clustering reduces the agglomeration criterion are subsequently grouped together.
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.
Benefits: The agglomerative technique is simple to use. It can generate an object ordering that may be useful for the display. There is no need to specify the number of clusters in agglomerative Clustering.
It depends on your definition of noise and outliers. Because the jagged edges (data points) of noisy data are close together, the single linkage is sensitive to any of these points.