Getting Started

This guide provides the essential steps to install denmune-skl and run your first clustering analysis.

Installation

The package can be installed from PyPI using pip.

pip install denmune-skl

Note

Until the project is accepted into scikit-learn-contrib and published, you must install it directly from the source repository.

From Source

To install the latest development version, clone the repository and install it locally:

git clone https://github.com/scikit-learn-contrib/denmune-skl.git
cd denmune-skl
pip install .

Basic Usage

The following example demonstrates how to use DenMune to cluster a simple non-convex dataset.

from sklearn.datasets import make_moons
from sklearn.preprocessing import StandardScaler
from denmune_skl import DenMune
import matplotlib.pyplot as plt

# 1. Generate and prepare data
X, y = make_moons(n_samples=250, noise=0.07, random_state=42)
X = StandardScaler().fit_transform(X)

# 2. Initialize and fit the model
# The k_nearest parameter is the main hyperparameter to tune.
model = DenMune(k_nearest=20, random_state=42)
labels = model.fit_predict(X)

# 3. Visualize the results
n_clusters = model.n_clusters_
print(f"Estimated number of clusters: {n_clusters}")

plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')
plt.title(f"DenMune Clustering (k={model.k_nearest})")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.show()

Where to Go Next

• To learn about the algorithm’s theory and parameter tuning, see the User Guide.

• For more detailed code examples, browse the Example Gallery.

• For detailed information on the class and its methods, consult the API Reference.