# HDBSCAN Projects at AI Tinkerers

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> Technology record last updated: 2026-07-24T06:06:18Z
> Generated: 2026-10-24T10:00:35Z

HDBSCAN: A robust, density-based clustering algorithm that finds clusters of varying densities and handles noise effectively.

HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a powerful clustering algorithm developed by Dr. Leland McInnes and his team. It extends DBSCAN by converting it into a hierarchical clustering algorithm, then uses a technique to extract a flat partitioning from the hierarchy. This allows HDBSCAN to discover clusters of varying densities in data, unlike algorithms such as k-means. It also inherently handles outliers, marking them as noise rather than forcing them into a cluster. Key advantages include its ability to work with arbitrary distance metrics and its minimal parameterization (primarily requiring only `min_cluster_size`), making it a versatile tool for exploratory data analysis and anomaly detection across diverse datasets.

- Official technology site: https://hdbscan.readthedocs.io/en/latest/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Datamaps are 2D maps which visualize hyperdimensional latent spaces](https://seattle.aitinkerers.org/talks/rsvp_K_XKHiLogDE)

- Event context: Frontier Builds Demo Night: Experiments at the Edge of AI — 2025-11-13 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_K_XKHiLogDE

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