sklekmeans: a scikit-learn extension

Date: Oct 08, 2025 Version: 0.2.1

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This site documents sklekmeans, a scikit-learn‑compatible implementation of Equilibrium K-Means (EKMeans) designed for robust clustering on imbalanced datasets. It covers installation and a quick start, algorithm details, full-batch, mini-batch, and semi-supervised training modes, optional numba acceleration, the full Python API compatible with sklearn estimators, and worked examples.

Getting started

Learn how to install, fit, and evaluate EKMeans on your data.

Quick Start

User guide

Concepts, guidance, and detailed usage of EKMeans and MiniBatchEKMeans.

User Guide

API reference

Full API reference for sklekmeans estimators, functions, and utilities.

API Reference

Examples

Practical examples demonstrating clustering on balanced and imbalanced datasets.

Examples Gallery

References

[1]

Y. He. An Equilibrium Approach to Clustering: Surpassing Fuzzy C-Means on Imbalanced Data, IEEE Transactions on Fuzzy Systems, 2025.

[2]

Y. He. Semi-supervised equilibrium K-means for imbalanced data clustering, Knowledge-Based Systems, 2025.

[3]

Y. He. Imbalanced Data Clustering Using Equilibrium K-Means, arXiv, 2024.