pam: PAM (Partitioning Around Medoids)

In fastkmedoids: Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA, FastCLARANS

Description

The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw; a largely equivalent method was also proposed by Whitaker in the operations research domain, and is well known by the name "fast interchange" there. (Schubert and Rousseeuw, 2019)

Usage

pam(rdist, n, k, maxiter = 0L)

Arguments

rdist	The distance matrix (lower triangular matrix, column wise storage)
n	The number of observations
k	The number of clusters to produce
maxiter	The maximum number of iterations (default: 0)

Value

KMedoids S4 class

References

L. Kaufman, P. J. Rousseeuw "Clustering by means of Medoids" Information Systems and Operational Research 21(2)

fastkmedoids documentation built on Jan. 22, 2021, 1:06 a.m.