mvkm-ed 1.1.0

Federated Multi-View K-Means Clustering with Rectified Gaussian Kernel

Fed-MVKM: Federated Multi-View K-Means Clustering with Rectified Gaussian Kernel

Overview

This package implements a combination of two advanced clustering algorithms:

1. Federated Multi-View K-Means Clustering (Fed-MVKM)
2. Rectified Gaussian Kernel Multi-View K-Means Clustering (MVKM-ED)

The implementation provides a privacy-preserving distributed learning framework for multi-view clustering while leveraging the enhanced discriminative power of rectified Gaussian kernels.

Key Features

• Privacy-preserving federated learning for multi-view data
• Automatic view importance weight learning
• Rectified Gaussian kernel for enhanced distance computation
• Efficient distributed computation
• Scalable implementation for IoT and edge devices
• Automatic parameter adaptation
• GPU acceleration support

Requirements

• Python 3.7+
• NumPy >= 1.19.0
• SciPy >= 1.6.0
• scikit-learn >= 0.24.0

Installation

pip install mvkm-ed

Usage

import numpy as np
from mvkm_ed import MVKMED, MVKMEDParams

# Create sample data
X1 = np.random.randn(100, 10)  # First view
X2 = np.random.randn(100, 15)  # Second view
X = [X1, X2]

# Set parameters
params = MVKMEDParams(
    cluster_num=3,
    points_view=2,
    alpha=2.0,
    beta=0.1,
    max_iterations=100,
    convergence_threshold=1e-4
)

# Create and fit model
model = MVKMED(params)
model.fit(X)

# Get cluster assignments
cluster_labels = model.index

Parameters

• cluster_num: Number of clusters
• points_view: Number of data views
• alpha: Exponent parameter to control view weights
• beta: Distance control parameter
• max_iterations: Maximum number of iterations
• convergence_threshold: Convergence criterion threshold

Citation

If you use this code in your research, please cite our papers:

@ARTICLE{10810504,
  author={Yang, Miin-Shen and Sinaga, Kristina P.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Federated Multi-View K-Means Clustering}, 
  year={2025},
  volume={47},
  number={4},
  pages={2446-2459},
  doi={10.1109/TPAMI.2024.3520708}
}

@article{sinaga2024rectified,
  title={Rectified Gaussian Kernel Multi-View K-Means Clustering},
  author={Sinaga, Kristina P. and others},
  journal={arXiv},
  year={2024}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

• Kristina P. Sinaga
• Email: [e-mail]

Acknowledgments

This work was supported by the National Science and Technology Council, Taiwan (Grant Number: NSTC 112-2118-M-033-004)