Agglomerative Clustering is a bottom-up approach in Machine Learning where each data point initially forms its own cluster. Pairs of clusters are then progressively merged based on similarity or distance measures, creating a hierarchy of clusters. This process continues until all points belong to a single cluster or a desired number of clusters is achieved.
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