clusx 0.3.1

Tool for clustering, analyzing, and benchmarking text data with advanced embeddings and statistical validation.

Clusterium

A toolkit for clustering, analyzing, and benchmarking text data using state-of-the-art embedding models and clustering algorithms.

Features

• Dirichlet Process Clustering: Implements the Dirichlet Process for text clustering
• Pitman-Yor Process Clustering: Implements the Pitman-Yor Process for text clustering with improved performance
• Evaluation: Evaluates clustering results using a variety of metrics, including Silhouette Score, Davies-Bouldin Index, and Power-law Analysis
• Visualization: Generates plots of cluster size distributions

Installation

For detailed installation instructions, please see the Installation Guide.

Quick Start

git clone https://github.com/sergeyklay/clusterium.git
cd clusterium
poetry install

Usage

For detailed usage instructions, use cases, examples, and advanced configuration options, please see the Usage Guide.

Quick Start

# Run clustering
clusx --input your_data.csv --column your_column --output clusters.csv

# Evaluate clustering results and generate visualizations
clusx evaluate \
  --input input.csv \
  --column your_column \
  --dp-clusters output_dp.csv \
  --pyp-clusters output_pyp.csv \
  --plot

Python API Example

from clusx.clustering import DirichletProcess
from clusx.clustering.utils import load_data_from_csv, save_clusters_to_json

# Load data
texts, data = load_data_from_csv("your_data.csv", column="your_column")

# Perform clustering
dp = DirichletProcess(alpha=1.0)
clusters, params = dp.fit(texts)

# Save results
save_clusters_to_json("clusters.json", texts, clusters, "DP", data)

Documentation

Full documentation is available at https://clusterium.readthedocs.io/.

License

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