Hdbscan implementation in JavaScript
TypeScript implementation of HDBSCAN clustering algorithm
HDBSCAN clustering algorithm implementation in TypeScript
Modular JS statistics toolkit for Node.js and the browser: descriptive stats, correlations (Pearson/Spearman/Kendall), t-tests & ANOVA (Student/Welch), reliability (Cronbach’s alpha), regression (linear/logistic), clustering (DBSCAN/HDBSCAN), and table/co
HDBSCAN clustering in Rust/WASM — ported from scikit-learn, optimized for 2D Euclidean distance
Hierarchical DBSCAN Clustering in JavaScript
Lightweight, provider-agnostic embedding utilities for Node.js
machine learning lib in javascript
Hierarchical DBSCAN Clustering in JavaScript
Given an email, return best-effort social profile URLs if publicly discoverable (Gravatar + heuristics).
Interactive 3D visualization of Mnemoverse memory graphs — atoms, Hebbian links, and consolidation dynamics
HDBSCAN clustering in pure Rust. A huge improvement on DBSCAN, capable of identifying clusters of varying densities.
HDBSCAN clustering algorithm, compatible with scikit-learn
HDBSCAN clustering — hierarchical density-based clustering with automatic noise detection
State-of-the-art clustering algorithms for Rust - surpassing scikit-learn, HDBSCAN, and RAPIDS cuML
State-of-the-art clustering algorithms for Rust - surpassing scikit-learn, HDBSCAN, and RAPIDS cuML
Analysis bounded context for 7sense bioacoustics platform - clustering, motif detection, sequence analysis
Dense clustering primitives (k-means, DBSCAN, HDBSCAN, EVoC, COP-Kmeans, DenStream, correlation clustering)
Embedding Vector Oriented Clustering — fast clustering of high-dimensional embedding vectors (Rust port of EVōC)
Metagenome assembled genome recovery from metagenomes using UMAP and HDBSCAN
Statistical analysis and data profiling engine with C FFI bindings.
Rust port of the EVoC clustering algorithm for high dimensional data
Face detection (bounding boxes, eyes/nose/mouth markers), and facial recognition (embeddings).
A comprehensive clustering toolkit for Ruby, providing UMAP, PCA, K-means, HDBSCAN and more. Built on top of annembed and hdbscan Rust crates for blazing-fast performance.
Extract topics from document embeddings using HDBSCAN clustering and c-TF-IDF term extraction. Provides automatic topic labeling, quality metrics, and support for various clustering algorithms.
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