Guarantees for Hierarchical Clustering by the Sublevel Set method
arXiv:2006.10274v22 citations
Originality Synthesis-oriented
AI Analysis
This work addresses the problem of ensuring robust hierarchical clustering for data analysis, but it is incremental as it builds on existing methods.
The paper extends the Sublevel Set method to hierarchical clustering, providing guarantees for near-optimal and approximately correct clusterings without distributional assumptions.
Meila (2018) introduces an optimization based method called the Sublevel Set method, to guarantee that a clustering is nearly optimal and "approximately correct" without relying on any assumptions about the distribution that generated the data. This paper extends the Sublevel Set method to the cost-based hierarchical clustering paradigm proposed by Dasgupta (2016).