### Differences Between KMeans and KMedoids algorithms.

Installing sklearn-extra.

Imports.

First, create a random sample of 2-d points. Add a single outlier far from every other point in the sample.

Initialize a KMeans function and a KMedoids function. I'm using the ones from SKLearn and SKLearn_extra.

Plot the clusters resulting from each clustering algorithm.

### Results

The KMeans() algorithm places the outlier into a cluster containing only itself, and sorts the remaining points into three distinct clusters. The KMedoids() algorithm puts the outlier into a cluster with the central points that are closest to it, rather than placing it into its own distinct group.