What is Agglomerative Clustering in Machine Learning?

Agglomerative clustering is a type of unsupervised machine learning algorithm that is used to group data points into clusters. It works by first assigning each data point to its own cluster and then merging the most similar clusters together. This process continues until all data points are grouped into a single cluster or until a predetermined number of clusters is reached. Agglomerative clustering is a bottom-up approach, meaning it starts with individual data points and works its way up to the larger clusters. The algorithm is most commonly used in applications such as market segmentation, image segmentation, and document clustering.