This function performs non hierarchical clustering on the basis of dissimilarity with Density-based Spatial Clustering of Applications with Noise (DBSCAN)
Usage
nhclu_dbscan(
dissimilarity,
index = names(dissimilarity)[3],
minPts = NULL,
eps = NULL,
plot = TRUE,
algorithm_in_output = TRUE,
...
)
Arguments
- dissimilarity
the output object from
dissimilarity()
orsimilarity_to_dissimilarity()
, or adist
object. If adata.frame
is used, the first two columns represent pairs of sites (or any pair of nodes), and the next column(s) are the dissimilarity indices.- index
name or number of the dissimilarity column to use. By default, the third column name of
dissimilarity
is used.- minPts
a
numeric
value or anumeric
vector specifying the minPts argument of dbscan::dbscan()). minPts is the minimum number of points to form a dense region. By default, it is set to the natural logarithm of the number of sites indissimilarity
. See details for guidance on choosing this parameter.- eps
a
numeric
value or anumeric
vector specifying the eps argument of dbscan). eps specifies how similar points should be to each other to be considered a part of a cluster. See details for guidance on choosing this parameter.- plot
a
boolean
indicating if the k-nearest neighbor distance plot should be plotted.- algorithm_in_output
a
boolean
indicating if the original output of dbscan should be returned in the output (TRUE
by default, see Value).- ...
you can add here further arguments to be passed to
dbscan()
(see dbscan).
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
dbscan.
Details
The dbscan (Density-based spatial clustering of
applications with noise) clustering algorithm clusters points on the basis
of the density of neighbours around each data points. It necessitates two
main arguments, minPts
, which stands for the minimum number of points to
identify a core, and eps
, which is the radius to find neighbors.
minPts
and eps
should be defined by the user, which is not
straightforward.
We recommend reading the help in dbscan)
to learn how to set these arguments, as well as the paper
(Hahsler et al. 2019)
. Note that clusters with a value of 0
are points which were deemed as noise by the algorithm.
By default the function will select values for minPts
and eps
. However,
these values can be inadequate and the users is advised to tune these values
by running the function multiple times.
Choosing minPts: how many points should be necessary to make a cluster? i.e., what is the minimum number of sites you expect in a bioregion? Set a value sufficiently large for your dataset and your expectations.
Choosing eps: how similar should sites be in a cluster? If eps
is
too small, then a majority of points will be considered too distinct and
will not be clustered at all (i.e., considered as noise)? If the value is
too high, then clusters will merge together.
The value of eps
depends on the minPts
argument, and the literature
recommends to choose eps
by identifying a knee in the k-nearest neighbor
distance plot. By default
the function will try to automatically find a knee in that curve, but the
result is uncertain, and so the user should inspect the graph and modify
dbscan_eps
accordingly. To explore eps
values, follow the
recommendation by the function when you launch it a first time without
defining eps
. Then, adjust depending on your clustering results.
Author
Boris Leroy (leroy.boris@gmail.com), Pierre Denelle (pierre.denelle@gmail.com) and Maxime Lenormand (maxime.lenormand@inrae.fr)
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_dbscan(dissim, index = "Simpson")
#> Trying to find a knee in the curve to search for an optimal eps value...
#> NOTE: this automatic identification of the knee may not work properly
#> if the curve has knees and elbows. Please adjust eps manually by
#> inspecting the curve, identifying a knee as follows:
#>
#> /
#> curve /
#> ___________/ <- knee
#> elbow -> /
#> /
#> /
clust2 <- nhclu_dbscan(dissim, index = "Simpson", eps = 0.2)
clust3 <- nhclu_dbscan(dissim, index = "Simpson", minPts = c(5, 10, 15, 20),
eps = c(.1, .15, .2, .25, .3))