Hierarchical clustering is a method used in data analysis to group similar objects or data points together based on their characteristics. It involves creating a hierarchy of clusters, where each cluster contains one or more data points that are most similar to each other. The process starts with the largest possible cluster and then recursively splits it into smaller groups until the desired level of detail is reached. Hierarchical clustering can be used for various purposes such as identifying patterns in large datasets, visualizing relationships between different variables or categories, and finding clusters of similar data points that may not be immediately apparent.