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What are some of the possible linkage types to use in order to form successive clusters?

Updated: March 26, 2023

Ward: The Ward linkage forms successive clusters by finding the clusters that minimize the increase in within-cluster variance at each step. It is the default option in sklearn’s hierarchical clustering API.
Single: The next cluster is formed by merging the observation that has the shortest minimum distance to an existing cluster.
Complete: The next cluster is formed by merging the observation that has the shortest maximum distance to an existing cluster.
Average: The next cluster is formed by merging the observation with the shortest average distance to an existing cluster.

Note: In divisive clustering, clusters are formed by dividing existing clusters that maximize the choice of linkage, essentially working inversely to what was described for each option.

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What are some of the possible linkage types to use in order to form successive clusters?

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