Fit MeanShift

Mean shift clustering using a flat kernel. Mean shift clustering aims to discover “blobs” in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids. Seeding is performed using a binning technique for scalability.

Parameters

Clusterer [string]

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

Default:

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

meanShift = MeanShift()
clusterer = make_pipeline(StandardScaler(), meanShift)

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:FitMeanshift:

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

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

meanShift = MeanShift()
clusterer = make_pipeline(StandardScaler(), meanShift)

    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