Available Hierarchies

Once a similarity tree is built, it can be transformed into a clustering hierarchy. These hierarchies generalize well-known clustering paradigms such as \(k\)-means and \(k\)-median, enabling fine-grained control over how cluster centroids and merges are computed.

We support all possible \((k,z)\)-hierarchies, allowing flexibility in choosing the most suitable hierarchy for a given dataset.

• \(z = 0\) → \(k\)-center (actually in theory: \(z = ∞\), but in this implementation we use 0 for \(∞\))

• \(z = 1\) → \(k\)-median

• \(z = 2\) → \(k\)-means

• …