Description Usage Arguments Value References Examples
graph.cem
clusters graphs following an expectation-maximization algorithm based
on the Kullback-Leibler divergence between the spectral densities of the
graph and of the random graph model.
1 | graph.cem(g, model, k, max_iter = 10, ncores = 1, bandwidth = "Sturges")
|
g |
a list containing the graphs or their adjacency matrices to be clustered. |
model |
a string that indicates one of the following random graph models: "ER" (Erdos-Renyi random graph), "GRG" (geometric random graph), "KR" (k regular graph), "WS" (Watts-Strogatz model), and "BA" (Barabasi-Albert model). |
k |
an integer specifying the number of clusters. |
max_iter |
the maximum number of expectation-maximization steps to execute. |
ncores |
the number of cores to be used for the parallel processing. The default value is 1. |
bandwidth |
string showing which criterion is used to choose the bandwidth during the spectral density estimation. Choose between the following criteria: "Silverman" (default), "Sturges", "bcv", "ucv" and "SJ". "bcv" is an abbreviation of biased cross-validation, while "ucv" means unbiased cross-validation. "SJ" implements the methods of Sheather & Jones (1991) to select the bandwidth using pilot estimation of derivatives. |
a list containing three fields:
labels a vector of the same length of g containing the clusterization labels;
a vector containing the estimated parameters for the groups. It has the
length equals to k
;
Celeux, Gilles, and Gerard Govaert. "Gaussian parsimonious clustering models." Pattern recognition 28.5 (1995): 781-793.
Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. _Journal of the Royal Statistical Society series B_, 53, 683-690. http://www.jstor.org/stable/2345597.
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