K-Medoids, a partitional clustering algorithm, is particularly valuable for clustering data points into K clusters, where K is a user-defined parameter. The primary distinction between K-Medoids and K-Means lies in the choice of cluster representatives. In K-Medoids, these representatives are actual data points, known as “medoids,” as opposed to the arithmetic means or centroids used in K-Means. This key difference makes K-Medoids more robust to outliers and noisy data because it minimizes the influence of extreme values on cluster formation.
K-Medoids is a clustering algorithm that is part of the broader K-means family of clustering techniques. However, instead of relying on centroids as reference points, K-Medoids uses actual data points as representatives of clusters, making it more robust to outliers and noise. K-Medoids is particularly well-suited for scenarios where cluster centers need to be real observations, ensuring that clusters are anchored to actual data points, which can be especially valuable in fields such as biology, medicine, and pattern recognition.
The algorithm operates as follows: it starts by selecting K initial data points as the initial medoids. It then assigns each data point to the nearest medoid, forming initial clusters. Next, it iteratively evaluates the total dissimilarity of each data point to its cluster medoid. If a different data point within the same cluster serves as a better medoid, it is swapped, which can lead to more representative medoids. This process continues until there is minimal or no change in medoids, indicating convergence. K-Medoids often outperforms K-means when dealing with data points that are not easily represented by a centroid, as it provides more robust clustering results.
K-Medoids is valuable in various fields, including biology, where it can be used to identify representative biological samples, and in pattern recognition for robust cluster formation. Its ability to anchor clusters to real data points enhances the interpretability of results and makes it a useful tool for clustering when the true data structure is not well-suited to centroid-based approaches like K-Means.