Kmeans Python Packages

kmeans1d

A Python package for optimal 1D k-means clustering.

neurocaps

A Python package for performing Co-Activation Patterns (CAPs) analyses on resting-state and task-based fMRI data.

betula-cluster

Memory-bounded clustering for large embeddings and tabular streams: BETULA cluster features in a Rust core, then k-means / GMM / Ward / spectral / Leiden / HDBSCAN on the compressed summary. Streaming partial_fit, scikit-learn API, PyO3, no LAPACK or SciPy at runtime.

gap-stat

Dynamically get the suggested clusters in the data for unsupervised learning.

superkmeans

⚡ Super fast clustering for high-dimensional vectors on CPUs (x86, ARM) and GPUs — for Python and C++. Faster clustering of vector embeddings than FAISS

markov-kmeans

Early stopping for K-means: stop when only boundary points still switch clusters, and settle them from their own label history. Shorter runs, uncertainty flags, soft memberships. Exact Lloyd, mini-batch and EMA (constant-step) training. scikit-learn compatible.

clumppy

Clustering algorithms

pdc-dp-means

"Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation" [Dinari and Freifeld, UAI 2022]

colorz

:art: A k-means color scheme generator.

pycave

Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

allmeans

Automatic topic modelling using minimal external input and computational resources

cake-ensemble

Confidence in Assignments via K-partition Ensembles

libkmcuda

Large scale K-means and K-nn implementation on NVIDIA GPU / CUDA

optimalcluster

OptimalCluster is the Python implementation of various algorithms to find the optimal number of clusters. The algorithms include elbow, elbow-k_factor, silhouette, gap statistics, gap statistics with standard error, and gap statistics without log. Various types of visualizations are also supported.

constrainedkmeans

Constrained KMeans algorithm.

metacluster

MetaCluster: An Open-Source Python Library for Metaheuristic-based Clustering Problems

hgpack

Source code of HG-means clustering, from "HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering". (Gribel and Vidal, 2019)

pytrain

Machine Learning library for python

vectors-of-locally-aggregate-concepts

Vectors of Locally Aggregated Concepts

pek

Progressive Ensemble K-means clustering

vlac

Vectors of Locally Aggregated Concepts

rectokens

[Pytorch] Efficient tokenization for recommendations and generative retrieval. Inspired by STATIC decoding from "Vectorizing the Trie"

flash-kmeans-mlx

IO-aware batched K-Means for Apple Silicon, ported from Flash-KMeans (Triton/CUDA) to pure MLX. Up to 94x faster than sklearn.

clus

Clustering and fuzzy-clustering library with integrated CLI