pdc-dp-means 0.0.8

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Parallel Delayed Cluster DP-Means

Paper

Introduction

The PDC-DP-Means package presents a highly optimized version of the DP-Means algorithm, introducing a new parallel algorithm, Parallel Delayed Cluster DP-Means (PDC-DP-Means), and a MiniBatch implementation for enhanced speed. These features cater to scalable and efficient cluster analysis where the number of clusters is unknown.

In addition to offering major speed improvements, the PDC-DP-Means algorithm supports an optional online mode for real-time data processing. Its scikit-learn-like interface is user-friendly and designed for easy integration into existing data workflows. PDC-DP-Means outperforms other nonparametric methods, establishing its efficiency and scalability in the realm of clustering algorithms.

See the paper for more details.

Installation

pip install pdc-dp-means

Quick Start

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()

One thing to note is that we replace the \lambda parameter from the paper with delta in the code, as lambda is a reserved word in python.

Usage

Please refer to the documentation: https://pdc-dp-means.readthedocs.io/en/latest/

Paper Code

Please refer to https://github.com/BGU-CS-VIL/pdc-dp-means/tree/main/paper_code for the code used in the paper.

Citing this work

If you use this code for your work, please cite the following:

@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}
}

License

Our code is licensed under the BDS-3-Clause license.