Single-Link clustering clearly explained | by Harika Bonthu | Medium

As we all know, Hierarchical Agglomerative clustering starts with treating each observation as an individual cluster, and then iteratively merges clusters until all the data points are merged into a single cluster. Dendrograms are used to represent hierarchical clustering results. Clusters are merged based on the distance between them and to calculate the distance between the clusters we have different types of linkages. It determines the distance between sets of observations as a function of the pairwise distance between observations. In this article, we aim to understand the Clustering process using the Single Linkage Method. Begin with importing necessary libraries Let us create toy data using numpy.random.random_sample Once we generate the random data points, we will create a pandas data frame. A glance at our toy data. Looks clean. Let us jump into the clustering steps. Step1: Visualize the data using a Scatter Plot Step2: Calculating the distance matrix in Euclidean method using

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