Spectral Bridges is a Python package that implements a novel clustering algorithm combining k-means and spectral clustering techniques. It leverages efficient affinity matrix computation and merges clusters based on a connectivity measure inspired by SVM's margin concept. This package is designed to provide robust clustering solutions, particularly suited for large datasets.
- Spectral Bridges Algorithm: Integrates k-means and spectral clustering with efficient affinity matrix calculation for improved clustering results.
- Scalability: Designed to handle large datasets by optimizing cluster formation through advanced affinity matrix computations.
- Customizable: Parameters such as number of clusters, iterations, and random state allow flexibility in clustering configurations.
- Model selection: Automatic model selection for number of nodes (m) according to a normalized eigengap metric.
Starting with version 1.0.0, Spectral Bridges not only utilizes FAISS's efficient k-means implementation but also uses a scikit-learn method clone for centroid initialization which is much faster (over 2x improvement).
You can install the package via pip:
pip install spectral-bridgesimport spectralbridges as sb
import numpy as np
# Generate sample data
np.random.seed(0)
X = np.random.rand(100, 10) # Replace with your dataset
# Initialize and fit Spectral Bridges (with a specified number of nodes if needed) and random seed
model = sb.SpectralBridges(n_clusters=5, random_state=42)
# Define range of nodes to evaluate, iterable or a single int
n_nodes_range = [10, 15, 20]
# Find the optimal number of nodes for a given value of clusters
# Modifies the instance attributes, returns a dict
# If n_nodes_range is None, then the model selects using self.n_nodes if not None
mean_ngaps = model.fit_select(X, n_nodes_range)
print("Optimal number of nodes:", model.n_nodes)
print("Dict of mean normalized eigengaps:", mean_ngaps)
# Predict clusters for new data points
new_data = np.random.rand(20, 10) # Replace with new data
predicted_clusters = model.predict(new_data)
print("Predicted clusters:", predicted_clusters)
# With a custom number of nodes
custom_model = sb.SpectralBridges(n_clusters=5, n_nodes=12, p=1) # And a p-bridge affinity
# Fit the model
custom_model.fit(X)
# Predict the same way...
custom_predicted_clusters = custom_model.predict(new_data)
print("Predicted clusters:", custom_predicted_clusters)
