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.