Connectivity-based clustering is also known as hierarchical clustering, where clustering analysis builds the cluster in an hierarchy. This method of clustering the dataset is considered not very suitable, especially when the dataset has too many outliers. Plotting the outliers in hierarchical clustering is complex and the computation process is time-consuming when the dataset is large.
In this section, we will oversee the implementation of hierarchical clustering using the same worlddata dataset in R. In order to implement hierarchical clustering, we first need to compute the distance for the elements in the dataset. We compute the distance between each and every element in the dataset using the dist function; this function takes the dataset as well as the method as an input, where we pass the methodology by which the distance is computed. This method can be used only for a numeric matrix. The different methods in which the distance is computed are euclidean...
