Description Usage Arguments Value
Performs k-means based subspace clustering. Center of each cluster is some number of principal components. Similarity measure is R^2 coefficient.
1 2 3 | mlcc.kmeans(X, number.clusters = 2, stop.criterion = 1, max.iter = 40,
max.subspace.dim = 4, initial.segmentation = NULL,
estimate.dimensions = 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 |
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 |
A list consisting of:
segmentation |
a vector containing the partition of the variables |
pcas |
a list of matrices, basis vectors for each cluster (subspace) |
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