Hierarchical clustering: One of the most important methods in unsupervised learning is Hierarchical clustering. In Hierarchical clustering for a given set of data points, the output is produced in the form of a binary tree (dendrogram). In the binary tree, the leaves represent the data points while internal nodes represent nested clusters of various sizes. Each object is assigned a separate cluster. Evaluation of all the clusters takes place based on a pairwise distance matrix. The distance matrix will be constructed using distance values. The pair of clusters with the shortest distance must be considered. The identified pair should then be removed from the matrix and merged together. The merged clusters' distance must be evaluated with the other clusters and the distance matrix should be updated. The process is to be repeated until the distance matrix is reduced to a single element.
An ordering of the objects is produced by hierarchical clustering. This helps with informative...
