Papers › Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE
Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE
Isaac Robinson, Emma Pierce-Hoffman
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Clustering | Coil-20 | Tree-SNE | NMI | .958 | #2 of 6 | Archive leaderboard | report |
| Image Clustering | MNIST-full | Tree-SNE | NMI | 0.864 | #16 of 16 | Archive leaderboard | report |
| Image Clustering | USPS | Tree-SNE | NMI | 0.885 | #12 of 16 | Archive leaderboard | report |
| Image Clustering | coil-100 | Tree-SNE | NMI | 0.926 | #7 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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