The Density-sensitive Self-stabilization of Independent Gaussian Mixtures (DSIGM) Clustering Algorithm is a novel algorithm that seeks to identify ideal clusters in data that allows for predictive classifications. DSIGM can be conceptualized as a two layer clustering algorithm. The base layer is a Self-stabilizing Gaussian Mixture Model (SGMM) that identifies the mixture components of the underlying distribution of data. This is followed by a top layer clustering algorithm that seeks to group these components into clusters in a density sensitive manner. The result is a clustering that allows for variable and irregularly shaped clusters that can sensibly categorize new data assumed to be part of the same distribution.
More details regarding DSIGM can be found in the documentation here.
dsigm requires:
numpy
scipy
sklearn
dsigm is tested and supported on Python 3.4+ up to Python 3.7. Usage on other versions of Python is not guaranteed to work as intended.
dsigm can be easily installed using pip
pip install dsigm
For more details on usage, see the documentation here.
See the changelog for a history of notable changes to dsigm.
dsigm is still under development. As of 0.3.1, only the Self-stabilizing Gaussian Mixture Model (SGMM) has been implemented.
There are three main branches for development and release. master is the current development build; staging is the staging branch for releases; release is the current public release build.
Documentation for dsigm can be found here.
Issues and Questions should be posed to the issue tracker here.