arXiv:2605. 23540v2 Announce Type: replace Abstract: Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data.
By Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich
arXiv:2506. 08725v3 Announce Type: replace-cross Abstract: Misuses of t-SNE and UMAP in visual analytics have become increasingly common.
By Hyeon Jeon, Jeongin Park, Sungbok Shin, Jinwook Seo
arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.
By Elizabeth Coda, Ery Arias-Castro, Gal Mishne
arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.
By Mahdi Shamsi, Soosan Beheshti
The paper formalizes a geometric tradeoff between ambient separation and sampling gaps to determine when distinct manifold components can be reliably separated in clustering. It introduces a threshold phenomenon for mutual‑k‑nearest‑neighbor graphs, defining an uncertainty zone where the number of clusters cannot be identified. The authors propose Manifold‑Based Clustering (MBC), which outputs a bracket interval quantifying this uncertainty rather than forcing a single cluster count.
By Savik Kinger, Luciano Dyballa, Steven W. Zucker
arXiv:2609.26748v1 Announce Type: cross
Abstract: Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters,...
By Siyi Wang, Alexandre Leblanc, Paul D. McNicholas