The k-medoids algorithm is extended from the k-means algorithm to decrease the sensitivity to the outlier data points.
Given the dataset D and the predefined parameter k, the k-medoids algorithm or the PAM algorithm can be described as shown in the upcoming paragraphs.
As per a clustering related to a set of k medoids, the quality is measured by the average distance between the members in each cluster and the corresponding representative or medoids.
An arbitrary selection of k objects from the initial dataset of objects is the first step to find the k medoids. In each step, for a selected object and a nonselected node , if the quality of the cluster is improved as a result of swapping ...