Metadata-Version: 2.4
Name: kbc-clustering
Version: 0.1.0
Summary: KBC: Isolation-Kernel + Binary Connected-component Clustering
Author-email: Hang Zhang <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/IsolationKernel/Kernel-bounded-clustering-versus-spectral-clustering
Project-URL: Repository, https://github.com/IsolationKernel/Kernel-bounded-clustering-versus-spectral-clustering
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=2.2.6
Requires-Dist: scipy>=1.15
Requires-Dist: scikit-learn>=1.7
Requires-Dist: torch>=2.8
Requires-Dist: tqdm>=4.67
Dynamic: license-file

# KBC Clustering

Kernel-Bounded Clustering: Achieving the Objective of Spectral Clustering without Eigendecomposition

## Installation

```bash
pip install kbc-clustering


```python
from kbc import KBC
import numpy as np

X = np.random.rand(1000, 50)
model = KBC(k=5, tau=0.4, psi=64, random_state=42)
labels = model.fit_predict(X)


## Reference
@article{ZHANG2025104440,
title = {Kernel-Bounded Clustering: Achieving the Objective of Spectral Clustering without Eigendecomposition},
journal = {Artificial Intelligence},
pages = {104440},
year = {2025},
issn = {0004-3702},
doi = {https://doi.org/10.1016/j.artint.2025.104440},
url = {https://www.sciencedirect.com/science/article/pii/S0004370225001596},
author = {Hang Zhang and Kai Ming Ting and Ye Zhu},
