Agglomerative Clustering is a type of hierarchical clustering method used to group similar data points into clusters. It starts with each data point as its own cluster and then iteratively merges the closest pairs of clusters based on a distance metric, such as Euclidean distance. This process continues until all points are combined into a single cluster or a specified number of clusters is reached. The result of Agglomerative Clustering can be visualized using a dendrogram, which is a tree-like diagram that shows the arrangement of clusters and the distances at which they were merged. This method is particularly useful for exploratory data analysis, as it helps identify natural groupings within the data.