Agglomerative clustering is a hierarchical clustering technique used in unsupervised machine learning. It operates on the principle of merging individual data points into larger clusters based on their similarity. Initially, each data point is treated as a separate cluster. The algorithm then iteratively combines the two closest clusters until only one large cluster remains or a specified number of clusters is achieved. This bottom-up approach allows for the visualization of the clustering process through dendrograms, which illustrate the relationships between clusters at various levels of granularity. Agglomerative clustering is widely used in various applications, including data analysis and pattern recognition.