K-Means++
Fast KMeans++ initialization.
A scikit-learn-compatible KMeans implementation with fast SIMD distance computations and parallel centroid initialization.
fastkmeanspp is a Python package that implements a KMeans clone from
scikit-learn with a faster KMeans++ centroid
initialization. It is designed to be a drop-in replacement for
scikit-learn's KMeans when initialization is the bottleneck.
✨ Features
- Fast KMeans++ initialization: Uses optimized squared-distance computations while selecting candidate centroids.
- SIMD fused operations: Uses Google Highway for portable vectorized fused multiply-add distance calculations.
- Parallel initialization: Computes distance rows in parallel with Highway's thread pool.
- scikit-learn compatibility: Provides familiar
fit,predict,labels_,cluster_centers_, andinertia_interfaces. - FAISS clustering: Uses FAISS for the Lloyd iterations after initialization.
Highway supplies the low-level fused operations and portable SIMD dispatch used by the distance kernel. Its thread pool splits distance rows across workers, so the same KMeans++ selection logic can use multiple CPU cores without changing the estimator interface.
🚀 Installation
python -m pip install fastkmeanspp
🔧 Usage
import numpy as np
from fastkmeanspp import KMeans
X = np.array([[0.0, 0.0], [0.1, 0.2], [4.0, 4.0], [4.2, 3.9]])
model = KMeans(n_clusters=2, random_state=42)
model.fit(X)
print(model.labels_)
print(model.cluster_centers_)
Set n_jobs=1 for serial centroid initialization or n_jobs=-1 to use all
available threads.
📖 Learn More
For tutorials and the API reference, visit the
fastkmeanspp documentation.
The MNIST tutorial compares clustering quality and
runtime with scikit-learn for K=10 and K=100.
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