A density-sensitive hierarchical clustering method
This work addresses a specific issue in clustering algorithms for data analysis, but it appears incremental as it builds on existing single linkage methods with theoretical modifications.
The authors tackled the chaining effect in hierarchical clustering by introducing $\\alpha$-unchaining single linkage ($SL(\\alpha)$) and a modified version $SL^*(\\alpha)$, which are sensitive to data density and provide theoretical solutions to this problem.
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.