Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering

TL;DR

This paper introduces branching embedding, a fast dimensionality reduction method that visualizes hierarchical clustering structures in 2D, aiding interpretation of high-dimensional data.

Contribution

The paper presents a new heuristic algorithm that efficiently converts dendrograms into 2D visualizations, preserving hierarchical structures.

Findings

Moderately preserves original hierarchical structures

Computationally efficient when hierarchical clustering is precomputed

Useful for visualizing high-dimensional data structures

Abstract

This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be beneficial for the case where hierarchical clustering is already performed. Numerical experiments revealed that the outputs of the algorithm moderately preserve the original hierarchical structures.