Everything on Hierarchical Clustering

In this article, you will learn. Clustering is the most common form of unsupervised learning on unlabeled data to clusters objects with common characteristics into discrete clusters based on a distance measure. Hierarchical Clustering is either bottom-up, referred to as Agglomerative clustering, or Divisive, which uses a top-down approach. A bottom-up approach where each data point is considered a singleton cluster at the start, clusters are iteratively merged based on similarity until all data points have merged into one cluster. Agglomerative clustering agglomerates pairs of clusters based on maximum similarity calculated using distance metrics to obtain a new cluster, thus reducing the number of clusters with every iteration.