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A robust EM clustering algorithm for Gaussian mixture models

Project description

A Robust EM Clustering Algorithm for Gaussian Mixture Models


Python implementation of Robust EM Clustering for Gaussian Mixture Models[1]. (Click here to view the paper for more detail.)

  • robustgmm.robustgmm

    Scikit-learn API style for Robust GMM

  • robustgmm.generator

    Generator for synthetic data from mixture of gaussian.

For more detail to use, see the example below or

  • Reference

    MS Yang, A robust EM clustering algorithm for gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961


  1. Install from PyPI

    pip install robustgmm
  2. Install from Github

    pip install git+


All examples are conducted to compare with the experimental results of the paper.

# For more detail, refer ./test/
import numpy as np

from robustgmm import RobustGMM
from robustgmm import Generator_Multivariate_Normal

# Generate data from 2 multivariate normal distribution with fixed random seed
real_means = np.array([[.0, .0], [20, .0]])
real_covs = np.array([[[1, .0], [.0, 1]],
                      [[9, .0], [.0, 9]]])
mix_prob = np.array([.5, .5])
generator = Generator_Multivariate_Normal(means=real_means,
X = generator.get_sample(800)

# GMM using robust EM Algorithm
rgmm = RobustGMM()

Figures for each examples in paper

  1. Example 1

    example1-1 example1-2

  2. Example 2

    example2-1-1 example2-1-2 example2-2-1 example2-2-2

  3. Example 3

    example3-1 example3-2

  4. Example 4


  5. Example 5

    example5-1 example5-2

  6. Example 6

    example6-1 example6-2

  7. Example 7


  8. Computational time cost


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