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.

Fed-MVKM is a novel privacy-preserving distributed learning framework designed for multi-view clustering that:

• Enables collaborative learning across distributed clients
• Preserves data privacy during the learning process
• Effectively handles heterogeneous data distributions
• Achieves robust clustering performance
• Implements adaptive weight learning mechanisms

Fed-MVKM/
├── Fed-MVKM-py/        # Python implementation
│   ├── mvkm_ed/        # Core Python package
│   ├── examples/       # Tutorials and examples
│   └── tests/         # Unit tests
└── matlab/            # MATLAB implementation
    ├── src/           # Source code
    └── examples/      # Example scripts

• 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

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

This package is officially published and verified on the Python Package Index (PyPI). You can:

• View the package at: https://pypi.org/project/mvkm-ed/
• Check release history at: https://pypi.org/project/mvkm-ed/#history
• Download statistics: https://pypistats.org/packages/mvkm-ed

pip install mvkm-ed

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

from mvkm_ed import FedMVKMED, FedMVKMEDParams

# Create client data
client_data = {
    'client1': [np.random.randn(100, 10), np.random.randn(100, 15)],
    'client2': [np.random.randn(100, 10), np.random.randn(100, 15)]
}

# Set federated parameters
fed_params = FedMVKMEDParams(
    cluster_num=3,
    points_view=2,
    alpha=2.0,
    beta=0.1,
    gamma=0.04,  # Federation parameter
    privacy_level=0.8
)

# Create and fit federated model
fed_model = FedMVKMED(fed_params)
fed_model.fit(client_data)

# Get global clustering results
global_labels = fed_model.get_global_labels()

The DHA dataset is an RGB-D multi-modal dataset for human action recognition and retrieval. This dataset represents a practical application of our federated multi-view clustering approach in action recognition using both depth and RGB information.

• Actions: 23 different action categories
• Subjects: 21 different subjects performing actions
• Views: Two complementary data views:
  • Depth data (6144-dimensional feature vectors)
  • RGB data (110-dimensional feature vectors)

For detailed information about the dataset, please refer to the paper: "Human action recognition and retrieval using sole depth information" (View Paper)

from mvkm_ed import FedMVKMED, FedMVKMEDParams
from mvkm_ed.datasets import load_dha

# Load DHA dataset with multiple views (depth and RGB)
X_dha, y_true = load_dha()  # Returns depth (6144-d) and RGB (110-d) features

# Split data for federated setup across different locations
client_data = {
    'site1': [X_dha[0][:150], X_dha[1][:150]],  # First 150 samples
    'site2': [X_dha[0][150:300], X_dha[1][150:300]],  # Next 150 samples
    'site3': [X_dha[0][300:], X_dha[1][300:]]  # Remaining samples
}

# Configure federated learning
fed_params = FedMVKMEDParams(
    cluster_num=23,  # Number of action categories
    points_view=2,  # Depth and RGB views
    alpha=2.0,
    beta=0.1,
    gamma=0.05,
    privacy_level=0.9
)

# Train federated model
fed_model = FedMVKMED(fed_params)
fed_model.fit(client_data)

# Evaluate clustering results
results = fed_model.evaluate(metrics=['nmi', 'ari'])
print(f"NMI Score: {results['nmi']:.3f}")
print(f"ARI Score: {results['ari']:.3f}")

• 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

• gamma: Federation parameter for client model updating
• privacy_level: Level of privacy preservation (0-1)
• communication_rounds: Maximum number of federation rounds
• client_tolerance: Convergence tolerance for client updates

1. Initialization Stage:

   • Set up central server
   • Initialize client configurations
   • Distribute initial parameters

2. Client Stage:

   • Local model optimization
   • View weight adaptation
   • Privacy preservation

3. Federation Stage:

   • Global model aggregation
   • Parameter synchronization
   • Convergence check

4. Finalization Stage:

   • Model evaluation
   • Results aggregation
   • Performance metrics computation

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}
}

@misc{sinaga2024rectifiedgaussiankernelmultiview,
      title={Rectified Gaussian kernel multi-view k-means clustering}, 
      author={Kristina P. Sinaga},
      year={2024},
      eprint={2405.05619},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2405.05619}, 
}

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

• Kristina P. Sinaga
• Email: [email protected] (The email address [email protected] is no longer under my authority. Please do not use it to contact me.)

This work was supported by:

• The National Science and Technology Council, Taiwan (Grant Number: NSTC 112-2118-M-033-004)
• GitHub Copilot for enhancing development efficiency and code quality
• The open-source community for their invaluable tools and libraries

Special thanks to GitHub Copilot for making the implementation process more efficient and helping to transform theoretical concepts into production-ready code. Its assistance significantly contributed to the development of both MATLAB and Python implementations.