cluster_algorithm: Wrapper function to perform partition around medioids (PAM)...

In harpomaxx/GSgalgoR: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer

Description

In GSgalgoR, the partition around medioids (PAM) algorithm is the default clustering process used under the evolutionary process.

Usage

cluster_algorithm(c, k)

Arguments

c	a dissimilarity matrix object of type 'dist'
k	positive integer specifying the number of clusters, less than the number of observations

Details

The function runs the pam function of the 'cluster' package with options cluster.only =TRUE, diss = TRUE, do.swap=TRUE, keep.diss=FALSE, keep.data = FALSE, pamonce= 2

Value

Returns a 'list' with the value '$cluster' which contains the cluster assignment of each of the samples evaluated

References

• Reynolds, A., Richards, G., de la Iglesia, B. and Rayward-Smith, V. (1992) Clustering rules: A comparison of partitioning and hierarchical clustering algorithms; Journal of Mathematical Modelling and Algorithms 5, 475–504. 10.1007/s10852-005-9022-1.

• Erich Schubert and Peter J. Rousseeuw (2019) Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms; Preprint, (https://arxiv.org/abs/1810.05691).

Examples

# load example dataset
require(iC10TrainingData)
require(pamr)
data(train.Exp)

calculate_distance <- select_distance(distancetype = "pearson")
Dist <- calculate_distance(train.Exp)
k <- 4
Pam <- cluster_algorithm(Dist, k)
table(Pam$cluster)

harpomaxx/GSgalgoR documentation built on Oct. 25, 2020, 3:47 p.m.