# Hierarchical clustering: visualization, feature importance and model selection

arXiv: https://arxiv.org/abs/2112.01372 · HTML: https://mlsanity.com/paper/2112.01372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation](https://mlsanity.com/paper/1704.08095/) (2017)
- [Clustering strategy and method selection](https://mlsanity.com/paper/1503.02059/) (2015)
- [NLS: an accurate and yet easy-to-interpret regression method](https://mlsanity.com/paper/1910.05206/) (2019)

## Built on by (most cited first, 2 of 2)

- [Hierarchical and Density-based Causal Clustering](https://mlsanity.com/paper/2411.01250/) (2024)
- [Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping](https://mlsanity.com/paper/2404.17886/) (2024)

## Abstract (arXiv)

We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram. Specifically, we propose a loss for choosing between clustering methods, a feature importance score and a graphical tool for visualizing the segmentation of features in a dendrogram. Current approaches to these tasks lead to loss of information since they require the user to generate a single partition of the instances by cutting the dendrogram at a specified level. Our proposed methods, instead, use the full structure of the dendrogram. The key insight behind the proposed methods is to view a dendrogram as a phylogeny. This analogy permits the assignment of a feature value to each internal node of a tree through an evolutionary model. Real and simulated datasets provide evidence that our proposed framework has desirable outcomes and gives more insights than state-of-art approaches. We provide an R package that implements our methods.