{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tree-sne-hierarchical-clustering-and","title":"Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE","arxiv_id":"2002.05687","date":"2020-02-13","proceeding":null,"authors":["Isaac Robinson","Emma Pierce-Hoffman"],"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.","url_abs":"https://arxiv.org/abs/2002.05687v1","url_pdf":"https://arxiv.org/pdf/2002.05687v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tree-sne-hierarchical-clustering-and","repo_url":"https://github.com/isaacrob/treesne","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-clustering-on-coil-20","task":"Image Clustering","dataset":"Coil-20","model":"Tree-SNE","rank_in_archive_order":2,"of":6,"metrics":{"NMI":".958"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-mnist-full","task":"Image Clustering","dataset":"MNIST-full","model":"Tree-SNE","rank_in_archive_order":16,"of":16,"metrics":{"NMI":"0.864"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-usps","task":"Image Clustering","dataset":"USPS","model":"Tree-SNE","rank_in_archive_order":12,"of":16,"metrics":{"NMI":"0.885"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-coil-100","task":"Image Clustering","dataset":"coil-100","model":"Tree-SNE","rank_in_archive_order":7,"of":10,"metrics":{"NMI":"0.926"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}