Skip to main content

A scikit-learn compatible python/cython implementation of the GMD algorithm.

Project description

Travis AppVeyor Codecov CircleCI ReadTheDocs

Scikit-learn Greedy Maximum Deviation (GMD) Algorithm

This project provides a scikit-learn compatible python implementation of the algorithm presented in [Trittenbach2018] together with some usage examples and a reproduction of the results from the paper.

Recent approaches in outlier detection seperate the subspace search from the actual outlier detection and run the outlier detection algorithm on a projection of the original feature space. See [Keller2012]. As a result the detection algorithm (Local Outlier Factor is used in the paper) does not suffer from the curse of dimensionality.

Refer to the documentation to see usage examples.

Project details

Release history Release notifications

This version


Download files

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

Files for gmd, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size gmd-0.0.1.tar.gz (93.5 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

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