The k-medoids method is a classical partitioning technique of clustering that splits a data set of n objects into k clusters, where the k number of clusters is assumed to be known a priori (which implies that the programmer must specify k before the execution of a k-medoids algorithm). The "goodness" of the given value of k can be assessed with methods such as the silhouette method.
The k-medoids method is a classical partitioning technique of clustering that splits a data set of n objects into k clusters, where the k number of clusters is assumed to be known a priori (which implies that the programmer must specify k before the execution of a k-medoids algorithm). The "goodness" of the given value of k can be assessed with methods such as the silhouette method.
Source: Wikipedia "K-medoids" · CC BY-SA 4.0
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