mvkm-ed 1.0.0

Rectified Gaussian Kernel Multi-View K-Means Clustering Algorithm

MVKM-ED: Rectified Gaussian Kernel Multi-View K-Means Clustering

Overview

MVKM-ED is a Python implementation of the Rectified Gaussian Kernel Multi-View K-Means Clustering algorithm. This algorithm effectively handles multiple views of data while automatically learning view importance weights.

Features

• Privacy-preserving multi-view clustering
• Adaptive view weight learning
• Rectified Gaussian kernel for distance computation
• Automatic parameter adaptation
• Efficient implementation with NumPy

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:

@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)