Linkage
In Exercise 2.01, Building a Hierarchy, you implemented hierarchical clustering using what is known as Centroid Linkage. Linkage is the concept of determining how you can calculate the distances between clusters and is dependent on the type of problem you are facing. Centroid linkage was chosen for Exercise 2.02, Applying Linkage Criteria, as it essentially mirrors the new centroid search that we used in k-means. However, this is not the only option when it comes to clustering data points. Two other popular choices for determining distances between clusters are single linkage and complete linkage.
Single Linkage works by finding the minimum distance between a pair of points between two clusters as its criteria for linkage. Simply put, it essentially works by combining clusters based on the closest points between the two clusters. This is expressed mathematically as follows:
dist(a,b) = min( dist( a[i]), b[j] ) )
In the preceding code, a[i] is the ith point within...
