Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering
This work addresses the need for efficient visualization of hierarchical clustering results, but it is incremental as it builds on existing clustering methods without introducing a fundamentally new paradigm.
The paper tackled the problem of visualizing high-dimensional data by proposing a new dimensionality reduction algorithm called branching embedding (BE), which converts a dendrogram into a two-dimensional scatter plot to reveal inherent structures, with numerical experiments showing that it moderately preserves original hierarchical structures.
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.