Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE

• URL: http://arxiv.org/abs/2002.05687v1
• Date: Thu, 13 Feb 2020 18:11:00 GMT
• Title: Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE
• Authors: Isaac Robinson, Emma Pierce-Hoffman
• Abstract summary: Tree-SNE is a hierarchical clustering and visualization algorithm based on stacked one-dimensional t-SNE embeddings. alpha-clustering recommends the optimal cluster assignment, without foreknowledge of the number of clusters. We demonstrate the effectiveness of tree-SNE and alpha-clustering on images of handwritten digits, mass-CyTOF data from blood cells, and single-cell RNA-sequencing (scRNA-seq) data from retinal cells.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology. Building on recent advances in speeding up t-SNE and obtaining finer-grained structure, we combine the two to create tree-SNE, a hierarchical clustering and visualization algorithm based on stacked one-dimensional t-SNE embeddings. We also introduce alpha-clustering, which recommends the optimal cluster assignment, without foreknowledge of the number of clusters, based off of the cluster stability across multiple scales. We demonstrate the effectiveness of tree-SNE and alpha-clustering on images of handwritten digits, mass cytometry (CyTOF) data from blood cells, and single-cell RNA-sequencing (scRNA-seq) data from retinal cells. Furthermore, to demonstrate the validity of the visualization, we use alpha-clustering to obtain unsupervised clustering results competitive with the state of the art on several image data sets. Software is available at https://github.com/isaacrob/treesne.

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