K-Medoids is a partitional clustering algorithm that falls under the category of unsupervised machine learning techniques. Clustering algorithms aim to group similar data points together into clusters based on some similarity or dissimilarity measure. K-Medoids, in particular, focuses on finding representative data points within each cluster, called “medoids,” to define cluster centers.
K-Medoids differs from the more well-known K-Means clustering algorithm. In K-Means, the cluster center is defined as the mean (average) of the data points in the cluster, whereas in K-Medoids, the cluster center is a real data point chosen from the dataset. This makes K-Medoids more robust to outliers, as the medoid is less affected by extreme values.
K-Medoids is used in various fields, including biology (for gene expression clustering), customer segmentation in marketing, image processing, and recommendation systems. It’s particularly suitable for cases where finding a single representative data point within each cluster is essential.
K-Medoids is typically used with distance or dissimilarity measures such as Euclidean distance, Manhattan distance, or other similarity metrics. Variants of K-Medoids exist, including PAM (Partitioning Around Medoids) and CLARA (Clustering Large Applications) for dealing with large datasets.