Hierarchical clustering: visualization, feature importance and model selection

Luben M. C. Cabezas; Rafael Izbicki; Rafael B. Stern

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arXiv · 2112.01372

Hierarchical clustering: visualization, feature importance and model selection

Luben M. C. Cabezas·Rafael Izbicki·Rafael B. Stern

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Abstract

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.

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BibTeX RIS

Luben M. C. Cabezas, Rafael Izbicki, Rafael B. Stern. 2021-11-30. Hierarchical clustering: visualization, feature importance and model selection. https://arxiv.org/abs/2112.01372

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