Skip to main content
Help us improve PyPI by participating in user testing. All experience levels needed!

DEnsity-BAsed CLustering

Project description

DeBaCl is an open-source Python library for DEnsity-BAsed CLustering with level set trees.

Level set trees are a statistically-principled way to represent the topology of a probability density function. This representation is particularly useful for several core tasks in statistics:

  • clustering, especially for data with multi-scale clustering behavior
  • describing data topology
  • exploratory data analysis
  • data visualization
  • anomaly detection

DeBaCl is a Python implementation of the Level Set Tree method, with an emphasis on computational speed, algorithmic simplicity, and extensibility. The code is available at:

https://github.com/CoAxLab/DeBaCl

and the API documentation is at:

https://debacl.readthedocs.org/en/v1.1/

Project details


Release history Release notifications

This version
History Node

1.1

History Node

1.0

History Node

0.2.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
debacl-1.1-py2-none-any.whl (25.6 kB) Copy SHA256 hash SHA256 Wheel py2 Jan 31, 2016
debacl-1.1.tar.gz (22.5 kB) Copy SHA256 hash SHA256 Source None Jan 31, 2016

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page