This modified k-means algorithm is similar to that described by Mather and Koch (2011). The main difference between the traditional k-means and this technique is that the user does not need to specify the desired number of classes/clusters prior to running the tool. Instead, the algorithm initializes with a very liberal overestimate of the number of classes and then merges classes that have cluster centres that are separated by less than a user-defined threshold. The main difference between this algorithm and the ISODATA technique is that clusters can not be broken apart into two smaller clusters.

## Reference

Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: an introduction. John Wiley & Sons.

## See Also

k_means_clustering

## Function Signature

`def modified_k_means_clustering(self, input_rasters: List[Raster], output_html_file: str = "", num_start_clusters: int = 1000, merge_distance: float = 1.0, max_iterations: int = 10, percent_changed_threshold: float = 2.0) -> Raster: ...`

## Project Links

WbW Homepage User Manual Support WbW