In the previous posts of this series, I covered methods to determine the optimal number of clusters, how k-means and k-medoids clustering algorithms work in detail. In this post, I will try to explain Hierarchical Clustering algorithm. First, I will explain what Hierarchical Clustering is, then I …

In the previous post of this series, I covered how k-means clustering works in detail. In this post, I will try to explain the k-medoids algorithm, also known as Partitioning Around Medoids. First, I will explain what k-medoids is, then I will explain how it works step by step, and then I will …

The center of each cluster, or centroid, in k-means clustering corresponds to the mean of the points allocated to the cluster. The fundamental principle of k-means clustering is to define clusters with the goal of minimizing total intra-cluster variation, also referred to as total within-cluster …