embc: General pourpose multivariate binary Clustering (EMbC)

In EMbC: Expectation-Maximization Binary Clustering

General pourpose multivariate binary Clustering (EMbC)

Description

embc implements the core function of the Expectation-Maximization multivariate binary clustering.

Usage

embc(X, U = NULL, stdv = NULL, maxItr = 200, info = 0)

Arguments

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.

Value

Returns a binClst object.

Examples

# -- apply EMbC to the example set of data points x2d ---
mybc <- embc(x2d@D)

EMbC documentation built on Oct. 3, 2023, 5:07 p.m.