Aggregate the resulting clustering of the SOM algorithm into super-clusters.
superClass(sommap, method, members, k, h, clustering = NULL, ...)# S3 method for somSC
print(x, ...)
# S3 method for somSC
summary(object, ...)
# S3 method for somSC
plot(
x,
what = c("obs", "prototypes", "add"),
type = c("dendrogram", "grid", "hitmap", "lines", "meanline", "barplot", "boxplot",
"mds", "color", "poly.dist", "pie", "graph", "dendro3d", "projgraph"),
plot.var = TRUE,
show.names = TRUE,
names = 1:prod(x$som$parameters$the.grid$dim),
...
)
# S3 method for somSC
projectIGraph(object, init.graph, ...)
cutree(object, k = NULL, h = NULL)
The superClass method returns an object of class somSC,
which is a list of the following elements:
The super clustering of the prototypes (only if either
k or h are given by user).
An hclust object.
The somRes object given as argument (see
trainSOM for details).
The projectIGraph method returns an object of class
is_igraph with the following attributes:
layoutprovides the layout of the projected graph according to the center of gravity of the super-clusters positioned on the SOM grid (graph attribute);
name and sizerespectively are the vertex number on the grid and the number of vertexes included in the corresponding cluster (vertex attribute);
weightgives the number of edges (or the sum of the weights) between the vertexes of the two corresponding clusters (edge attribute).
A somRes object.
Argument passed to the hclust function.
Argument passed to the hclust function.
Argument passed to the cutree function (number
of super-clusters to cut the dendrogram).
Argument passed to the cutree function (height
at which to cut the dendrogram).
Precomputed clustering provided by user. In this case, the
function just returns a somSC object with this clustering but no
associated dendrogram. Not all methods and plots apply to this case.
Used for plot.somSC: further arguments passed either to
the function plot (case type = "dendro") or to
plot.myGrid (case type = "grid") or to
plot.somRes (all other cases).
A somSC object.
A somSC object.
What to plot. Can be either the observations (obs), the
prototypes (prototypes), an additional variable (add), or
NULL if not appropriate.
Automatically set for types "hitmap" (to "obs") and "grid",
(to "prototypes"). Default to "obs" otherwise.
If what = "add", the function plot.somRes is also run
with the argument what set to "add".
The type of plot to draw. Default value is "dendrogram",
to plot the dendrogram of the clustering. Case "grid" plots the grid
with colors corresponding to the clusters of the super clustering. Case
"projgraph" uses an is_igraph object passed to the
argument variable and plots the projected graph as defined by the
method projectIGraph. All other cases are those available in the
function plot.somRes and superimpose the super-clusters over
these plots.
A boolean indicating whether a plot showing the evolution of
the explained variance should be plotted. This argument is only used when
type = "dendrogram", its default value is TRUE.
Whether the cluster titles must be printed in center of
the grid or not for type = "grid". Default to FALSE (titles not
displayed).
If show.names = TRUE, values of the title to
display for type="grid". Default to "Cluster " followed by the cluster
number.
An igraph object which is projected
according to the super-clusters. The vertices of init.graph must
correspond to the rows of the original dataset processed by SOM (note that
case "korresp" is not handled by this function). In the projected
graph, the vertices are positioned at the center of gravity of the
super-clusters (more details in the section Details below).
Élise Maigné elise.maigne@inrae.fr
Madalina Olteanu olteanu@ceremade.dauphine.fr
Nathalie Vialaneix nathalie.vialaneix@inrae.fr
The superClass method can be used in 2 ways:
to choose the number of super clusters via an hclust
object: then, both arguments k and h can be NULL. In
this case, superClass only returns the dendrogram of the
hierarchical clustering, which can then be cut with the method
cutree (to which either k or h must be specified);
to cut the clustering into super clusters. Then, either argument
k or argument h must be specified (see
cutree for details).
The squared distance between prototypes is passed to the algorithm.
summary on a superClass object produces a complete summary of
the results that displays the number of clusters and super-clusters, the
clustering itself and performs ANOVA analyses. For type = "numeric"
the ANOVA is performed for each input variable and test the difference of
this variable across the super-clusters of the map. For
type = "relational" a dissimilarity ANOVA is performed (see
(Anderson, 2001), except that in the present version, a crude estimate of the
p-value is used which is based on the Fisher distribution and not on a
permutation test.
On plots, the different super classes are identified in the following ways:
either with different color, when type is set among:
"grid" (N, K, R), "hitmap" (N, K, R), "lines"
(N, K, R), "barplot" (N, K, R), "boxplot", "poly.dist"
(N, K, R), "mds" (N, K, R), "dendro3d" (N, K, R),
"graph" (R), "projgraph" (R);
or with title, when type is set among: "color" (N, K),
"pie" (N, R).
In the list above, the charts available for a numerical SOM are
indicated with a N, with a K for a korresp SOM and with an R for
relational SOM.
projectIGraph produces a projected graph from the
is_igraph object passed to the argument variable as
described in (Olteanu and Villa-Vialaneix, 2015). The attributes of this
graph are the same than the ones obtained from the SOM map itself in the
function projectIGraph. plot.somSC used with
type = "projgraph" calculates this graph and represents it by
positioning the super-vertexes at the center of gravity of the
super-clusters. This feature can be combined with pie.graph = TRUE to
super-impose the information from an external factor related to the
individuals in the original dataset (or, equivalently, to the vertexes of the
graph).
Anderson M.J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26, 32-46.
Olteanu M., Villa-Vialaneix N. (2015) Using SOMbrero for clustering and visualizing graphs. Journal de la Societe Francaise de Statistique, 156, 95-119.
set.seed(11051729)
my.som <- trainSOM(x.data = iris[, 1:4])
# choose the number of super-clusters
sc <- superClass(my.som)
plot(sc)
# cut the clustering
sc <- superClass(my.som, k = 4)
summary(sc)
plot(sc)
plot(sc, type = "grid")
plot(sc, what = "obs", type = "hitmap")
# cut the clustering with a different number of clusters
sc <- superClass(my.som, k = 5)
summary(sc)
# provide a precomputed clustering
sc2 <- superClass(my.som, clustering = sample(1:3, 25, replace = TRUE))
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