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.

clumppy

Clustering algorithms

pycave

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

gap-stat

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

superkmeans

A Super fast K-Means library for High-Dimensional vectors on CPUs (x86, ARM) and GPUs

metacluster

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

allmeans

Automatic topic discovery with zero LLMs, minimal input

colorz

:art: A k-means color scheme generator.

pdc-dp-means

No description available

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.

cake-ensemble

CAKE: Confidence in Assignments via K-partition Ensembles

vectors-of-locally-aggregate-concepts

Tool for creating document features

constrainedkmeans

A constrained KMeans algorithm.

libkmcuda

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

hgpack

HG-means clustering package

rectokens

A tokenizer library for sequential recommendation systems

pytrain

Machinelearning library for python

flash-kmeans-mlx

IO-aware batched K-Means for Apple Silicon, ported from Flash-KMeans (Triton/CUDA) to pure MLX.

vlac

Tool for creating document features

qlearnkit

A python library for quantum machine learning and quantum deep learning, built on top of qiskit and pennylane

artutils

A modular ML-powered palette toolkit for artists and designers

hgmeans

HG-means clustering for minimum sum-of-squares formulation

pek

Progressive Ensemble K-means clustering