Grouping data using the similarity measures
The k-medoids algorithm is a variation of the k-means algorithm that uses medoids (actual data points) as representatives of each cluster instead of centroids. Unlike the k-means algorithm, which calculates the mean of the data points within each cluster, the k-medoids algorithm selects the most centrally located data point within each cluster as the medoid. This makes k-medoids more robust to outliers and suitable for data with non-Euclidean distances.
Here are some key differences between k-medoids and k-means:
- Representative points: In k-medoids, the representatives of each cluster are actual data points from the dataset (medoids), while in k-means, the representatives are the centroids, which are calculated as the mean of the data points.
- Distance measure: The distance measure used in k-means is typically the Euclidean distance. On the other hand, k-medoids can handle various distance measures, including non-Euclidean distances...
