Locally Adaptive Hierarchical Cluster Termination With Application To Individual Tree Delineation
This work addresses a specific bottleneck in clustering algorithms for applications like individual tree delineation, representing an incremental improvement over existing methods.
The authors tackled the problem of determining when to stop merging clusters in hierarchical clustering by proposing a locally adaptive termination procedure that adapts to the hierarchical tree structure, offering a multi-scale alternative to fixed threshold methods.
A clustering termination procedure which is locally adaptive (with respect to the hierarchical tree of sets representative of the agglomerative merging) is proposed, for agglomerative hierarchical clustering on a set equipped with a distance function. It represents a multi-scale alternative to conventional scale dependent threshold based termination criteria.