Welcome to PDC-DP-Means documentation!

PDC-DP-Means is a Python library for running fast, scalable DP-Means or Mini-Batch DP-Means. It is built on top of scikit-learn and numpy.

Check out the Usage section for further information, including how to install the project.

Quickstart

from sklearn.datasets import make_blobs
from pdc_dp_means import DPMeans

# Generate sample data
X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

# Apply DPMeans clustering
dpmeans = DPMeans(n_clusters=1,n_init=10, delta=10)  # n_init and delta parameters
dpmeans.fit(X)

# Predict the cluster for each data point
y_dpmeans = dpmeans.predict(X)

# Plotting clusters and centroids
import matplotlib.pyplot as plt

plt.scatter(X[:, 0], X[:, 1], c=y_dpmeans, s=50, cmap='viridis')
centers = dpmeans.cluster_centers_
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)
plt.show()

Contents

• Usage
  • Installation
• DPMeans
  • Parameters
  • Attributes
  • See Also
  • Notes
  • Examples
  • API
    • DPMeans
      • DPMeans.fit()
• MiniBatchDPMeans
  • Parameters
  • Attributes
  • API
    • MiniBatchDPMeans
      • MiniBatchDPMeans.fit()
      • MiniBatchDPMeans.partial_fit()
      • MiniBatchDPMeans.predict()

If you use this package for your reseach, please cite the following paper:

@inproceedings{dinari2022revisiting,
title={Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation},
author={Dinari, Or and Freifeld, Oren},
booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
year={2022}
}