K-Medoids Clustering is a popular clustering technique used in data analysis and machine learning to partition a dataset into distinct groups (clusters). Unlike K-Means, which uses the mean of cluster points as the center, K-Medoids selects actual data points as the centers, known as medoids. This method is particularly useful when dealing with datasets that contain noise or outliers, as the medoids are less affected by such anomalies.
The algorithm begins by randomly selecting a set of k medoids from the dataset. It then assigns each data point to the nearest medoid based on a defined distance metric, such as Euclidean distance. After all points are assigned, the algorithm updates the medoids by selecting the data point within each cluster that minimizes the total distance to all other points in that cluster. This process of assignment and updating continues iteratively until the medoids no longer change, indicating that the clusters are stable.
K-Medoids Clustering is beneficial in various applications, such as customer segmentation, image processing, and bioinformatics, due to its robustness against outliers and ability to produce interpretable results. However, it requires the number of clusters (k) to be specified in advance, which can sometimes be a limitation in practical applications. Techniques such as the silhouette method or the elbow method may be employed to help determine an appropriate number of clusters.