A density-sensitive hierarchical clustering method

We define a hierarchical clustering method: $α$-unchaining single linkage or $SL(α)$. The input of this algorithm is a finite metric space and a certain parameter $α$. This method is sensitive to the density of the distribution and offers some solution to the so called chaining effect. We also define a modified version, $SL^*(α)$, to treat the chaining through points or small blocks. We study the theoretical properties of these methods and offer some theoretical background for the treatment of chaining effects.

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