# Clustering (kmeans)¶

Several clustering methods are available in the EnMAP-Box. You can find them in the Processing Toolbox under EnMAP-Box ‣ Clustering. The usual way to apply these methods is to use a Fit … algorithm first and then apply it to an image with Predict Clustering.

This recipe demonstrates the basic workflow of applying clusterers using K-Means clustering (Fit KMeans) and the test dataset.

You can find all the available clustering algorithms here.

1. Open the test dataset

2. In the processing toolbox go to EnMAP-Box ‣ Clustering ‣ Fit KMeans

• Specify enmap_berlin.bsq under Raster
• Under Output Clusterer specify an output file path and click Run
3. Now open EnMAP-Box ‣ Clustering ‣ Predict Clustering

• Select enmap_berlin.bsq as input Raster
• Under Clusterer click and select the output .pkl file from the Fit KMeans algorithm
• Specify an output filepath for the transformed raster under Clustering and click Run

Tip

8 clusters is the default of the kmeans algorithm here, if you want to change the number of clusters, run the Fit Kmeans algorithm with a fewer number, by altering the KMeans() function in the Code window to KMeans(n_clusters=4). This will reduce the amount of clusters to 4.