Fit KMeans

K-Means clustering.

K-means clustering is an unsupervised machine learning algorithm used for partitioning a dataset into distinct, non-overlapping groups or clusters.K-means clustering aims to minimize the within-cluster variance or sum of squared distances from data points to their assigned cluster centroids.

Usage:

1. Open the algorithm from the processing toolbox.

2. Load an existing training dataset or create one by clicking the processing algorithm icon, then click run.

   

Parameters

Clusterer [string]

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

Default:

from sklearn.cluster import KMeans

clusterer = KMeans(n_clusters=8, n_init=10)

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:FitKmeans:

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

clusterer: Clusterer
    Default value:  from sklearn.cluster import KMeans

clusterer = KMeans(n_clusters=8, n_init=10)
    Argument type:  string
    Acceptable values:
            - String value
            - field:FIELD_NAME to use a data defined value taken from the FIELD_NAME field
            - expression:SOME EXPRESSION to use a data defined value calculated using a custom QGIS expression
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