geokmeans 0.1.1

Accelerated, compute efficient k-means clustering (Geometric-k-means and six other accelerated variants), with a scikit-learn-style API.

geokmeans

Fast, eco-friendly k-means clustering for Python. A thin, scikit-learn-style wrapper over a C++ library implementing seven k-means algorithms — including Geometric-k-means, the bound-free method from Sharma et al. (2026).

All seven algorithms return the same Lloyd's k-means solution; they differ in how aggressively they prune distance computations, so you get identical results with far less work.

Algorithm	algorithm=
Geometric-k-means	"geo" (default)
Lloyd's	"lloyd"
Elkan	"elkan"
Hamerly	"hamerly"
Annulus	"annulus"
Exponion	"exponion"
Ball k-means++	"ball"

Installation

pip install geokmeans

Prebuilt wheels are published for Linux, macOS, and Windows (CPython 3.9–3.13), so no compiler is required on common platforms.

Quickstart

import numpy as np
from geokmeans import GeoKMeans

X = np.random.default_rng(0).random((1000, 20))

km = GeoKMeans(n_clusters=8, algorithm="geo", random_state=0).fit(X)

km.labels_                     # cluster index per point  (n_samples,)
km.cluster_centers_            # centroids                (n_clusters, n_features)
km.n_iter_                     # iterations to convergence
km.n_distance_calculations_    # distance computations performed

km.predict(np.random.random((5, 20)))   # assign new points

The API mirrors scikit-learn (fit, predict, fit_predict, cluster_centers_, labels_, n_iter_), so it drops into existing pipelines.

Long-running jobs

The compute runs in C++ with the GIL released, so a long fit won't freeze the interpreter and you can run it off the main thread:

from concurrent.futures import ProcessPoolExecutor

def cluster(X):
    return GeoKMeans(n_clusters=50, algorithm="geo", random_state=0).fit(X)

with ProcessPoolExecutor() as pool:
    result = pool.submit(cluster, X).result()

Pass verbose=True to stream convergence progress.

Citation

If you use this package, please cite:

Sharma, P., Stanislaw, M., Kurban, H., Kulekci, O., & Dalkilic, M. (2026). Geometric-k-means: A Bound Free Approach to Fast and Eco-Friendly k-means. Machine Learning, 115(2), 30. https://doi.org/10.1007/s10994-025-06891-1

Documentation

Full documentation: https://parichit.github.io/Geometric-k-means/

License

Apache-2.0. See LICENSE.