Hierarchical clustering: visualization, feature importance and model selection

• URL: http://arxiv.org/abs/2112.01372v1
• Date: Tue, 30 Nov 2021 20:38:17 GMT
• Title: Hierarchical clustering: visualization, feature importance and model selection
• Authors: Luben M. C. Cabezas, Rafael Izbicki, Rafael B. Stern
• Abstract summary: We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram. The key insight behind the proposed methods is to view a dendrogram as a phylogeny. Real and simulated datasets provide evidence that our proposed framework has desirable outcomes.
• Score: 4.017760528208122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 ancestral state reconstruction. Real and simulated datasets provide evidence that our proposed framework has desirable outcomes. We provide an R package that implements our methods.

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