fdc — Fast Density Clustering
A Python package for clustering low-dimensional data using kernel density maps and density graphs. Ships with a Rust extension for 2–3x acceleration out of the box.
Features
- No cluster count required — automatically discovers the number of clusters
- Non-convex clusters — handles crescents, rings, and other complex shapes
- Multiscale — works with varying densities and population sizes
- Fast — O(n log n) time, O(n) memory via KD-tree search
- Rust-accelerated — optional native extension for 2–3x speedup (pre-built wheels for Linux, macOS, Windows)
- Two parameters — neighborhood size (auto) and noise threshold (eta)
Installation
pip install fdc
Pre-built wheels include the Rust extension. Falls back to pure Python if no wheel is available.
Quick start
from fdc import FDC
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
X, y = make_blobs(n_samples=5000, n_features=2, centers=10)
X = StandardScaler().fit_transform(X)
model = FDC(eta=0.5)
model.fit(X)
print(f"Found {len(model.idx_centers)} clusters")
Performance vs C-backed competitors (100K points, 2D)
| Algorithm | Runtime |
|---|---|
| FDC (Rust) | 1.8s |
| FDC (Python) | 4.8s |
| DBSCAN (sklearn/C) | 23.7s |
| HDBSCAN (sklearn/C) | 61.8s |
Links
Citation
@article{mehta2018high,
title={A high-bias, low-variance introduction to Machine Learning for physicists},
author={Mehta, Pankaj and Bukov, Marin and Wang, Ching-Hao and Day, Alexandre GR and Richardson, Clint and Fisher, Charles K and Schwab, David J},
journal={arXiv preprint arXiv:1803.08823},
year={2018}
}
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for fdc 1.17
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