K-Medoids is a clustering algorithm similar to K-Means, but it uses actual data points (called “medoids”) to represent clusters. It works as follows:
1. Initialization: Select K data points as initial medoids.
2. Assignment: Assign data points to the nearest medoid, forming K clusters.
3. Update Medoids: Choose the data point within each cluster that minimizes total dissimilarity as the new medoid.
4. Repeat: Iterate steps 2 and 3 until convergence.
K-Medoids is robust to outliers and is suitable for non-numeric or categorical data. It’s used in applications like image compression, document clustering, and customer segmentation to find representative points within clusters.