Hierarchical clustering: visualization, feature importance and model selection

Autor:	Cabezas, Luben M. C., Izbicki, Rafael, Stern, Rafael B.
Rok vydání:	2021
Předmět:	Statistics - Methodology Computer Science - Machine Learning I.5.3
Druh dokumentu:	Working Paper
Popis:	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. Comment: 29 pages, 9 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.01372 Zobrazit plný text záznamu View this record from Arxiv