The k-medoids problem is a clustering problem similar to k-means. The name was coined by Leonard Kaufman and Peter J. Rousseeuw with their PAM (Partitioning...

is minimal. Medoids are similar in concept to means or centroids, but medoids are always restricted to be members of the data set. Medoids are most commonly...

centers are medoids (as in k-medoids clustering) instead of arithmetic means (as in k-means clustering), this is also called the medoid-based silhouette...

input dataset. This algorithm is often confused with the k-medoids algorithm. However, a medoid has to be an actual instance from the dataset, while for...

Lance-Williams-equations is more efficient, while for other (Hausdorff, Medoid) the distances have to be computed with the slower full formula. Other linkage...

instance, better Euclidean solutions can be found using k-medians and k-medoids. The problem is computationally difficult (NP-hard); however, efficient...

values, they are set equal to the largest and smallest values that remain Medoid A representative object of a set X {\displaystyle {\mathcal {X}}} of objects...

between data points. Unlike clustering algorithms such as k-means or k-medoids, affinity propagation does not require the number of clusters to be determined...

which the outcome is forced to correspond to a member of the sample, is the medoid. There is no widely accepted standard notation for the median, but some...

PROCLUS uses a similar approach with a k-medoid clustering. Initial medoids are guessed, and for each medoid the subspace spanned by attributes with low...