Hierarchical clustering is a powerful unsupervised learning technique that groups data points into a hierarchy of clusters. Hierarchical clustering allows you to explore the data and discover the natural groupinas within it$.$One algorithm for clustering data is to group data into clusters each with $2,4,8$ points etc until we have reached a desired number of total clusters $($like k$).$ For the given data set above create a matrix of distances and start clustering until total final clusters is & You may use two rows from previous part and also the fact that the matrix is symmetric. So you only need to compute half the distances. Break ties using the point with smaller wad.

Identify $3$ clusters each with $2$ points

ill. Merge two of the $3$ 'elosest' clusters to get $2$ clusters.