KBC: Isolation-Kernel + Binary Connected-component Clustering
Project description
KBC Clustering
Kernel-Bounded Clustering: Achieving the Objective of Spectral Clustering without Eigendecomposition
Installation
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},
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
kbc_clustering-0.2.0.tar.gz
(5.7 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file kbc_clustering-0.2.0.tar.gz.
File metadata
- Download URL: kbc_clustering-0.2.0.tar.gz
- Upload date:
- Size: 5.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9245742c43e19e7e336cdd99b35cfcb59e90a72f6a948a00f7b7305458727741
|
|
| MD5 |
6f75aee3c880a83af080242cbd28723b
|
|
| BLAKE2b-256 |
e1a7b2eaea2969c0b7c084fe50bbad2d8609dd46bed40c3cef16647c6c5b33c2
|
File details
Details for the file kbc_clustering-0.2.0-py3-none-any.whl.
File metadata
- Download URL: kbc_clustering-0.2.0-py3-none-any.whl
- Upload date:
- Size: 6.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
040931f48e4ef659fb64e6095b151bdc676f3e3d38cb5044ec47d875ebb93c7c
|
|
| MD5 |
d48f162783b649532743ebbef33d623d
|
|
| BLAKE2b-256 |
6ea7a1314d40d4707cdb4f8d68410761ef93a084620a6aae6f7f2d5f67fa2ec6
|