nhclu_clarans {bioregion} | R Documentation |
Non hierarchical clustering: CLARANS
Description
This function performs non hierarchical clustering on the basis of dissimilarity with partitioning around medoids, using the Clustering Large Applications based on RANdomized Search (CLARANS) algorithm.
Usage
nhclu_clarans(
dissimilarity,
index = names(dissimilarity)[3],
seed = NULL,
n_clust = c(1, 2, 3),
numlocal = 2,
maxneighbor = 0.025,
algorithm_in_output = TRUE
)
Arguments
dissimilarity |
the output object from |
index |
name or number of the dissimilarity column to use. By default,
the third column name of |
seed |
for the random number generator (NULL for random by default). |
n_clust |
an |
numlocal |
an |
maxneighbor |
a positive |
algorithm_in_output |
a |
Details
Based on fastkmedoids package (fastclarans).
Value
A list
of class bioregion.clusters
with five slots:
name:
character
containing the name of the algorithmargs:
list
of input arguments as provided by the userinputs:
list
of characteristics of the clustering processalgorithm:
list
of all objects associated with the clustering procedure, such as original cluster objectsclusters:
data.frame
containing the clustering results
In the algorithm
slot, if algorithm_in_output = TRUE
, users can
find the output of
fastclarans.
Author(s)
Pierre Denelle (pierre.denelle@gmail.com), Boris Leroy (leroy.boris@gmail.com), and 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
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")
clust1 <- nhclu_clarans(dissim, index = "Simpson", n_clust = 5)
partition_metrics(clust1, dissimilarity = dissim,
eval_metric = "pc_distance")