Distribution-based methods group data points together based on their likelihood of belonging to the same probability distribution.

Examples

• Gaussian mixture model (GMM)

Gaussian mixture model (GMM)

How it works

• GMM assumes that every data point is generated from multiple Gaussian distributions with unknown parameters
• performs iterative Expectation Maximization (EM) steps to fit the data points
  • In the Expectation (E) step: data points are assigned to clusters that assume randomly selected Gaussian parameters
  • In the Maximization (M) step: the parameters of the Gaussian distribution are updated to best fit the data points in the cluster
• It allows each example to be a member of several clusters with different membership score