fdc 1.17

Fast Density Clustering in low-dimension

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

• GitHub
• Examples

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