Hierarchical Clustering
Hierarchical clustering is an unsupervised machine learning technique used to group similar data points into clusters by building a hierarchy of clusters, typically represented as a dendrogram. It does not require pre-specifying the number of clusters and can be agglomerative (bottom-up, merging clusters) or divisive (top-down, splitting clusters). This method is widely applied in fields like biology, marketing, and social sciences for exploratory data analysis and pattern discovery.
Developers should learn hierarchical clustering when working with datasets where the natural grouping structure is unknown or hierarchical, such as in gene expression analysis, document categorization, or customer segmentation. It is particularly useful for visualizing relationships through dendrograms and when the number of clusters is not predetermined, making it ideal for exploratory tasks in data science and machine learning projects.