Which type of hierarchical clustering starts with individual data points and groups them by similarity?

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Multiple Choice

Which type of hierarchical clustering starts with individual data points and groups them by similarity?

Agglomerative clustering is the bottom-up form of hierarchical clustering that starts with each data point as its own cluster and then merges the most similar pairs of clusters step by step. At each step, the two clusters that are closest according to a chosen linkage criterion (such as single-link, complete-link, average-link, or Ward) are combined, until all points fall into a single cluster or until a desired number of clusters remains. This approach creates a dendrogram that shows how clusters are nested within larger clusters, capturing the hierarchical structure of the data. Divisive clustering is the opposite, beginning with all points in one cluster and repeatedly splitting them. The general term hierarchical clustering covers both approaches, while initialization is not a clustering method.

Agglomerative clustering is the bottom-up form of hierarchical clustering that starts with each data point as its own cluster and then merges the most similar pairs of clusters step by step. At each step, the two clusters that are closest according to a chosen linkage criterion (such as single-link, complete-link, average-link, or Ward) are combined, until all points fall into a single cluster or until a desired number of clusters remains. This approach creates a dendrogram that shows how clusters are nested within larger clusters, capturing the hierarchical structure of the data. Divisive clustering is the opposite, beginning with all points in one cluster and repeatedly splitting them. The general term hierarchical clustering covers both approaches, while initialization is not a clustering method.

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