Hierarchical Clustering: This approach starts with all observations either belonging to their own cluster consisting of just that data point (agglomerative or bottom-up) or all observations belonging to one large cluster containing every data point (divisive or top-down). Clusters are formed on successive iterations by merging clusters that are most similar in the bottom-up approach or splitting those furthest apart in the top-down approach until it stabilizes at a number of clusters somewhere between 1 and the total number of observations in the dataset.
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