Aggregate the resulting clustering of the SOM algorithm into super-clusters.
superClass(sommap, method, members, k, h, ...)# 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, ...)
The superClass function 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.somSC function returns an object of class
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 where
to cut the dendrogram).
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 you want to plot for superClass object. Either the
observations (obs), the prototypes (prototypes) or an
additional variable (add), or NULL if not appropriate.
Automatically set for types "hitmap" (to "obs"), 'grid'
(to "prototypes"), default to "obs" otherwise.
If what='add', the function plot.somRes will be called 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
in color according to the super clustering. Case "projgraph" uses an
igraph object passed to the argument variable and plots
the projected graph as defined by the function projectIGraph.somSC.
All other cases are those available in the function plot.somRes
and surimpose the super-clusters over these plots.
A boolean indicating whether a graph 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 number of vertices of init.graph
must be equal to the number of rows in the original dataset processed by the
SOM (case "korresp" is not handled by this function). In the projected
graph, the vertices are positionned 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 function can be used in 2 ways:
to choose the number of super clusters via an hclust
object: then, both arguments k and h are not filled.
to cut the clustering into super clusters: then, either argument
k or argument h must be filled. See cutree for
details on these arguments.
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 marked
with a N, with a K for a korresp SOM and with a R for
relational SOM.
projectIGraph.somSC produces a projected graph from the
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.somRes. plot.somSC used with
type="projgraph" calculates this graph and represents it by
positionning 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")
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