Grouping data using agglomerative clustering

Before we talk about agglomerative clustering, we need to understand hierarchical clustering. Hierarchical clustering refers to a set of clustering algorithms that creates tree-like clusters by consecutively splitting or merging them, and they are represented using a tree. Hierarchical clustering algorithms can be either bottom-up or top-down. Now, what does this mean? In bottom-up algorithms, each datapoint is treated as a separate cluster with a single object. These clusters are then successively merged until all the clusters are merged into a single giant cluster. This is called agglomerative clustering. On the other hand, top-down algorithms start with a giant cluster and successively split these clusters until individual datapoints are reached.

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