Fit Birch

Implements the BIRCH clustering algorithm. It is a memory-efficient, online-learning algorithm provided as an alternative to MiniBatchKMeans. It constructs a tree data structure with the cluster centroids being read off the leaf. These can be either the final cluster centroids or can be provided as input to another clustering algorithm such as AgglomerativeClustering.

Parameters

Clusterer [string]

Scikit-learn python code. See Birch for information on different parameters.

Default:

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import Birch

birch = Birch(n_clusters=3)
clusterer = make_pipeline(StandardScaler(), birch)

Training dataset [file]

Training dataset pickle file used for fitting the clusterer. If not specified, an unfitted clusterer is created.

Outputs

Output clusterer [fileDestination]

Pickle file destination.

Command-line usage

>qgis_process help enmapbox:FitBirch:

----------------
Arguments
----------------

clusterer: Clusterer
    Default value:  from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import Birch

birch = Birch(n_clusters=3)
clusterer = make_pipeline(StandardScaler(), birch)
    Argument type:  string
    Acceptable values:
            - String value
dataset: Training dataset
    Argument type:  file
    Acceptable values:
            - Path to a file
outputClusterer: Output clusterer
    Argument type:  fileDestination
    Acceptable values:
            - Path for new file

----------------
Outputs
----------------

outputClusterer: <outputFile>
    Output clusterer