Clustering models options

GaussianMixture

class gemseo.mlearning.cluster.gaussian_mixture.GaussianMixture(data, transformer=None, var_names=None, n_components=5, **parameters)[source]

Gaussian mixture clustering algorithm.

Constructor.

Parameters
  • data (Dataset) – learning dataset.

  • transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.

  • var_names (list(str)) – names of the variables to consider.

  • n_components (int) – number of Gaussian mixture components. Default: 5.

  • parameters – Scikit-learn algorithm parameters.

KMeans

class gemseo.mlearning.cluster.kmeans.KMeans(data, transformer=None, var_names=None, n_clusters=5, random_state=0, **parameters)[source]

KMeans clustering algorithm.

Constructor.

Parameters
  • data (Dataset) – learning dataset.

  • transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.

  • var_names (list(str)) – names of the variables to consider.

  • n_clusters (int) – number of clusters. Default: 5.

  • random_state (int) – If None, use a random generation of the initial centroids. Use an int to make the randomness deterministic. Default: 0.

  • parameters – Scikit-learn algorithm parameters.