The k-medoids algorithm is a clustering technique that can be used on graph data. It offers advantages over traditional k-means for explaining cluster differences and can be applied directly to graph data by calculating the shortest paths between nodes. The key difference between k-medoids and k-means lies in how centers are defined, with medoids being actual data points rather than centroids. K-medoids is particularly useful for graph data because it allows for easy interpretation of clusters through their central reference points. However, it can be computationally expensive due to the need to calculate all pairwise distances between nodes. The Neo4j library provides a implementation of k-medoids that can be used on graph data, including a distance array generation function and a function to write results to Neo4j. The algorithm's effectiveness depends on the value of k, which should be chosen based on the silhouette score of each node in the cluster. A high silhouette score indicates a well-defined cluster with a dense central region and a gradual decrease in similarity towards the periphery. The algorithm can also be used on other types of data, such as genetic data, but may require adjustments due to differences in connectivity and data distribution. Overall, k-medoids is a valuable clustering technique that offers advantages over traditional methods for explaining cluster differences and can be applied to a variety of graph data sets.