Moving Up the Cluster Tree with the Gradient Flow
arXiv:2109.08362v23 citations
AI Analysis
This work provides theoretical insight for clustering researchers, but it is incremental as it connects existing approaches without new empirical results.
The paper establishes a correspondence between cluster tree and gradient flow clustering methods from the 1970s, showing that gradient ascent flow can move up the cluster tree.
The paper establishes a strong correspondence between two important clustering approaches that emerged in the 1970's: clustering by level sets or cluster tree as proposed by Hartigan and clustering by gradient lines or gradient flow as proposed by Fukunaga and Hostetler. We do so by showing that we can move up the cluster tree by following the gradient ascent flow.