1st Nov 2023

K-Medoids, or Partitioning Around Medoids (PAM), is a clustering algorithm used in data analysis and machine learning. It’s employed to group similar data points into clusters, with a strong focus on robustness and the ability to handle outliers. Unlike K-Means, K-Medoids doesn’t use the mean as cluster centers; instead, it begins by selecting k initial data points as “medoids” or cluster representatives. Data points are assigned to the nearest medoid, forming clusters based on their proximity. The medoids are updated iteratively by selecting the data point that minimizes the total dissimilarity to other points within the same cluster, and this process continues until the clusters stabilize.

K-Medoids is especially useful when dealing with datasets that may contain outliers or when you want to pinpoint the most central or representative data points within clusters. Its robustness comes from using actual data points as medoids, reducing sensitivity to extreme values, which is a limitation of K-Means. In a practical application, you can use K-Medoids to cluster data, such as police shooting records, aiding in the identification of representative cases within each cluster, ultimately enhancing the reliability and insights derived from your analysis.