Unsupervised Learning Algorithm -  Hierarchical Clustering Hierarchical clustering Concepts Hierarchical clustering is a popular unsupervised machine learning algorithm used to cluster or group similar data points together in a dataset. Hierarchical clustering does not need the user to predetermine the number of clusters, in contrast to K-Means clustering. The algorithm works by creating a hierarchy of clusters, where each data point initially forms its own cluster, and clusters are successively merged based on their similarity. Here is an example of how Hierarchical clustering works : Suppose we have a dataset of customer transactions, where each transaction includes the customer's age, income, and spending behaviour. We want to group customers with similar spending behaviour together for targeted marketing campaigns. We use Hierarchical clustering to create a hierarchy of clusters based on the similarity of their spending behaviour. The algorithm initially assigns each customer