K-medoids is a partitioning-based clustering algorithm. It is related to the k-means but, instead of using the centroid as reference data point for the cluster, we use the medoid which is the individual nearest to all the other points within its cluster. One of the main consequence of this approach is that the resulting partition is less sensible to outliers.
This course material describes the algorithm. Then, we focus on the silhouette tool which can be used to determine the right number of clusters, a recurring open problem in cluster analysis.
Keywords: cluster analysis, clustering, unsupervised learning, paritionning method, relocation approach, medoid, PAM, partitioning aroung medoids, CLARA, clustering large applications, silhouette, silhouette plot
Slides: Cluster analysis - k-medoids algorithm
References:
Wikipedia, "k-medoids".
