cdc-cluster 0.2.1

A novel Clustering algorithm by measuring Direction Centrality (CDC) locally. It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points.

Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity (CDC)

We propose a novel Clustering algorithm by measuring Direction Centrality (CDC) locally. It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We present an interactive Demo and a brief introduction to the algorithm at https://zpguigroupwhu.github.io/CDC-Introduction-Website/, and develop a CDC toolkit at https://github.com/ZPGuiGroupWhu/ClusteringDirectionCentrality This paper has been published in Nature Communications, and more details can be seen https://www.nature.com/articles/s41467-022-33136-9.

Installation

Supported python versions are 3.8 and above.

This project has been uploaded to PyPI, supporting direct download and installation from pypi

pip install cdc-cluster

Manual Installation

git clone https://github.com/ZPGuiGroupWhu/CDC-pkg.git
cd CDC-pkg
pip install -e .

Usage

The CDC algorithm is refactored to be a scikit-learn compatible estimator. It provides both a class-based interface CDC and a function-based interface cdc_cluster.

Class-based Usage

from cdc_cluster import CDC
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons

# Generate sample data
X, _ = make_moons(n_samples=200, noise=0.05, random_state=42)

# Initialize and fit CDC
# n_neighbors: Number of nearest neighbors to consider (k_num)
# ratio: Ratio for determining the DCM threshold
cdc = CDC(n_neighbors=20, ratio=0.9)
cdc.fit(X)

# Get cluster labels
# Labels start from 0. Noisy samples are labeled as -1.
labels = cdc.labels_

# Plot result
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
plt.title("CDC Clustering Result")
plt.show()

Function-based Usage

from cdc_cluster import cdc_cluster
from sklearn.datasets import make_blobs

X, _ = make_blobs(n_samples=200, centers=3, random_state=42)

# Compute clustering directly
# Returns an array of cluster labels
labels = cdc_cluster(X, n_neighbors=20, ratio=0.9)

print(f"Number of clusters: {len(set(labels)) - (1 if -1 in labels else 0)}")

Citation Request:

Peng, D., Gui, Z.*, Wang, D. et al. Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nat. Commun. 13, 5455 (2022). https://www.nature.com/articles/s41467-022-33136-9

License

This project is covered under the MIT License.