File: README.md

SPINEX Clustering

SPINEX (Similarity-based Predictions and Explainable Neighbors Exploration) Clustering is an advanced clustering algorithm that combines multiple similarity measures with multi-level analysis capabilities.

Features

• Multiple similarity methods (correlation, spearman, kernel, cosine)
• Multi-level clustering capabilities
• Parallel processing support
• Explainable results with similarity and neighbor analysis
• Automatic threshold determination
• PCA dimensionality reduction option
• Comprehensive evaluation metrics

Installation

pip install spinex-clustering

Quick Start

from spinex_clustering import SPINEX_Clustering
import numpy as np

# Create sample data
X = np.random.randn(100, 10)

# Initialize and fit the clustering model
model = SPINEX_Clustering(
    threshold='auto',
    use_multi_level=True,
    enable_similarity_analysis=True
)

# Get cluster labels
labels = model.fit_predict(X)

# Print first few labels to verify it worked
print("First few cluster labels:", labels[:10])

Documentation

For detailed documentation and examples, visit [documentation link].

License

This project is licensed under the MIT License - see the LICENSE file for details.