Hierarchical Clustering

Hierarchical clustering is an unsupervised machine-learning technique used to group data points into clusters based on their similarity. It is commonly used for data analysis and data mining purposes. Hierarchical clustering works by creating a hierarchy of clusters using a bottom-up approach. It starts with each data point as its own single-element cluster and then successively merges pairs of clusters until one big cluster is formed. The result of a hierarchical clustering is a tree-like structure (or a dendrogram) that can be used to visualize the hierarchical structure of the data. It also helps to identify patterns in the data that may otherwise be difficult to see. Hierarchical clustering is an important tool for data analysis since it can help to identify relationships between different data points and can help to uncover hidden structure in data.