K-means: This method groups data into ‘k’ number of clusters by reducing the distance between data points and the center of their assigned cluster. The ‘means’ in the name refers to averaging the data points to find the center of the cluster. It’s good for spherical-shaped clusters.
K-medoids: Similar to K-means, but instead of using the mean, it uses actual data points as the center of the cluster, known as medoids. This method is more robust to noise and outliers compared to K-means because medoids are less influenced by extreme values.