7.6k citations · 8.1k across the 7 of their papers we have counts for
7 papers
Persistent Multiscale Density-based Clustering
Daniël Bot, Leland McInnes, Jan Aerts
Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumptions about the data. Density-bas…
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher +18
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working…
Improving Mapper's Robustness by Varying Resolution According to Lens-Space Density
Kaleb D. Ruscitti, Leland McInnes
We propose a modification of the Mapper algorithm that removes the assumption of a single resolution scale across semantic space and improves the robustness of the results under ch…
Parametric UMAP embeddings for representation and semi-supervised learning
Tim Sainburg, Leland McInnes, Timothy Q Gentner
UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured da…
Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy
Xin Li, Ondrej E. Dyck, Mark P. Oxley +5
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomi…
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes, John Healy, James Melville
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riema…
Accelerated Hierarchical Density Clustering
Leland McInnes, John Healy
We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement…