clusx 0.3.3

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

Clusterium is 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

Quick Start

pip install clusx

# 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)

Note

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

Project Information

Clusterium is released under the MIT License, its documentation lives at Read the Docs, the code on GitHub, and the latest release on PyPI. It’s rigorously tested on Python 3.11+.

If you’d like to contribute to Clusterium you’re most welcome!

Support

Should you have any question, any remark, or if you find a bug, or if there is something you can’t do with the Clusterium, please open an issue.