General pourpose multivariate binary Clustering (EMbC)
embc implements the core function of the Expectation-Maximization multivariate binary clustering.
embc(X, U = NULL, stdv = NULL, maxItr = 200, info = 0)
X |
The input data set. A multivariate matrix where each row is a data point and each column is an input feature (a variable). |
U |
A multivariate matrix with same dimension as X with the values of certainty associated to each corresponding value in X. Certainties assign reliability to the data points so that the less reliable is a data point the less its leverage in the clustering. By default certainties are set to one (no uncertainty in any value in X). |
stdv |
a vector with bounds for the maximum precision of clusters, given as minimum standard deviation for each variable, (by default is set to rep(sqrt(.Machine$double.eps),ncol(X)) |
maxItr |
A limit to the number of iterations in case of slow convergence (defaults to 200). |
info |
Level of information shown at each step: info=0 (default) shows step likelihood, number of clusters, and number of changing labels; info=1, include clustering statistics; info=2, include delimiters information; info<0, suppress any step information. |
Returns a binClst object.
# -- apply EMbC to the example set of data points x2d --- mybc <- embc(x2d@D)
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