Agglomerative Clustering is a type of hierarchical clustering, which is a broader concept that involves organizing data into a tree-like structure. A dendrogram is a diagram that represents this hierarchy and shows how clusters are formed through successive merges, providing a visual interpretation of the clustering process.

The effectiveness of Agglomerative Clustering depends significantly on the choice of distance metric, which determines how the similarity between data points is measured. Euclidean Distance is one of the most commonly used metrics, calculating the straight-line distance between points in a multi-dimensional space.

Single-linkage and complete-linkage are two different methods for determining the distance between clusters in Agglomerative Clustering. Single-linkage, also known as the nearest neighbor method, considers the shortest distance between any two points in the clusters, while complete-linkage, or the farthest neighbor method, considers the longest distance.

Ward's Method is another linkage criterion used in Agglomerative Clustering, focusing on minimizing the variance within clusters. The agglomeration schedule is a table that records the sequence of cluster mergers, the clusters involved, and the distance at which each merger occurs, providing detailed insights into the clustering process.

In cluster analysis, a cut-off threshold can be applied to a dendrogram to decide the number of clusters. By choosing a specific height on the dendrogram, researchers can determine where to cut the tree to form meaningful clusters, aiding in the interpretation of the data.