cluster_algorithm {GSgalgoR} | R Documentation |
In GSgalgoR
, the partition around medioids (PAM) algorithm is the
default clustering process used under the evolutionary process.
cluster_algorithm(c, k)
c |
a dissimilarity matrix object of type |
k |
positive integer specifying the number of clusters, less than the number of observations |
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
Returns a 'list'
with the value '$cluster'
which
contains the cluster assignment of each of the samples evaluated
Reynolds, A., Richards, G., de la Iglesia, B. and Rayward-Smith, V. (1992) Clustering rules: A comparison of partitioning and hierarchical clustering algorithms; Journal of Mathematical Modelling and Algorithms 5, 475–504. 10.1007/s10852-005-9022-1.
Erich Schubert and Peter J. Rousseeuw (2019) Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms; Preprint, (https://arxiv.org/abs/1810.05691).
# load example dataset require(iC10TrainingData) require(pamr) data(train.Exp) calculate_distance <- select_distance(distancetype = "pearson") Dist <- calculate_distance(train.Exp) k <- 4 Pam <- cluster_algorithm(Dist, k) table(Pam$cluster)