Description Usage Arguments Value References Examples
Performs k-means based subspace clustering. Center of each cluster is some number of principal components. This number can be fixed or estimated by PESEL. Similarity measure between variable and a cluster is calculated using BIC.
1 2 3 4 5 6 7 8 9 10 | mlcc.kmeans(
X,
number.clusters = 2,
stop.criterion = 1,
max.iter = 30,
max.subspace.dim = 4,
initial.segmentation = NULL,
estimate.dimensions = TRUE,
show.warnings = FALSE
)
|
X |
A matrix with only continuous variables. |
number.clusters |
An integer, number of clusters to be used. |
stop.criterion |
An integer indicating how many changes in partitions triggers stopping the algorithm. |
max.iter |
An integer, maximum number of iterations of k-means loop. |
max.subspace.dim |
An integer, maximum dimension of subspaces. |
initial.segmentation |
A vector, initial segmentation of variables to clusters. |
estimate.dimensions |
A boolean, if TRUE (value set by default) subspaces dimensions are estimated. |
show.warnings |
A boolean, if set to TRUE all warnings are displayed, default value is FALSE. |
A list consisting of:
segmentation |
a vector containing the partition of the variables |
pcas |
a list of matrices, basis vectors for each cluster (subspace) |
Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood, Piotr Sobczyk, Malgorzata Bogdan, Julie Josse
1 2 3 | sim.data <- data.simulation(n = 50, SNR = 1, K = 5, numb.vars = 50, max.dim = 3)
mlcc.res <- mlcc.kmeans(sim.data$X, number.clusters = 5, max.iter = 20, max.subspace.dim = 3)
show.clusters(sim.data$X, mlcc.res$segmentation)
|
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