u.PAM

Integer: Number of clusters to get

Logical: If TRUE, <code>x</code> should be a <code>dist</code> or dissimilarity matrix.
Otherwise, <code>x</code> should be a matrix of cases by features. Default = FALSE

diss

String: Dissimilarity metric to be used. Options: 'euclidean', 'manhattan'

metric

Logical: If TRUE, perform the swap phase (See <code>cluster::pam</code>), as in the
original PAM algorithm. This is computationally intensive and can be skipped. Default = TRUE

do.swap

Logical: If TRUE, print messages to screen

verbose

Additional parameters to be passed to <code>cluster::pam</code>

Perform PAM clustering using <code>cluster::pam</code>

Advanced Machine Learning and Visualization made efficient and accessible. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression, Survival Analysis), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and dynamic graphs.

u.PAM: Partitioning Around Medoids

Description

Usage

Arguments

See Also