nhclu_clara {bioregion} | R Documentation |
Non-hierarchical clustering: CLARA
Description
This function performs non-hierarchical clustering based on dissimilarity using partitioning around medoids, implemented via the Clustering Large Applications (CLARA) algorithm.
Usage
nhclu_clara(
dissimilarity,
index = names(dissimilarity)[3],
seed = NULL,
n_clust = c(1, 2, 3),
maxiter = 0,
initializer = "LAB",
fasttol = 1,
numsamples = 5,
sampling = 0.25,
independent = FALSE,
algorithm_in_output = TRUE
)
Arguments
dissimilarity |
The output object from |
index |
The name or number of the dissimilarity column to use. By
default, the third column name of |
seed |
A value for the random number generator (set to |
n_clust |
An |
maxiter |
An |
initializer |
A |
fasttol |
A positive |
numsamples |
A positive |
sampling |
A positive |
independent |
A |
algorithm_in_output |
A |
Details
Based on fastkmedoids package (fastclara).
Value
A list
of class bioregion.clusters
with five components:
name: A
character
string containing the name of the algorithm.args: A
list
of input arguments as provided by the user.inputs: A
list
of characteristics of the clustering process.algorithm: A
list
of all objects associated with the clustering procedure, such as original cluster objects (only ifalgorithm_in_output = TRUE
).clusters: A
data.frame
containing the clustering results.
If algorithm_in_output = TRUE
, the algorithm
slot includes the output of
fastclara.
Author(s)
Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)
Maxime Lenormand (maxime.lenormand@inrae.fr)
References
Schubert E & Rousseeuw PJ (2019) Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. Similarity Search and Applications 11807, 171-187.
See Also
For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a4_2_non_hierarchical_clustering.html.
Associated functions: nhclu_clarans nhclu_dbscan nhclu_kmeans nhclu_pam nhclu_affprop
Examples
comat <- matrix(sample(0:1000, size = 500, replace = TRUE, prob = 1/1:1001),
20, 25)
rownames(comat) <- paste0("Site",1:20)
colnames(comat) <- paste0("Species",1:25)
dissim <- dissimilarity(comat, metric = "all")
#clust <- nhclu_clara(dissim, index = "Simpson", n_clust = 5)