consensusMeansPerClVSkm: Calculate means per Cluster and view for Spherical k-Means by...
In mvc: Multi-View Clustering

Description Usage Arguments Value Examples

Calculate means per Cluster and view for Spherical k-Means by using a consensus approach.

1	consensusMeansPerClVSkm(view1, view2, view1Idx, view2Idx)

`view1`	data matrix (row-wise in unit length).
`view2`	data matrix (row-wise in unit length).
`view1Idx`	vector of length NROW(view1) with natural numbers 1..k, indicating cluster for each data vector of view1.
`view2Idx`	vector of length NROW(view1) with natural numbers 1..k, indicating cluster for each data vector of view2.

cluster means as matrices per view (row-wise in unit length).

{
view1 = structure(c(1, 1, -1, 0, 1, 0, -1, -1), .Dim = c(4L, 2L))
view2 = structure(c(1, 1, -1, 0, 1, 0, -1, 0), .Dim = c(4L, 2L))
view1Idx = c(2, 2, 1, 1)
view2Idx = c(2, 1, 1, 1)
mPerClV=consensusMeansPerClVSkm(view1,view2,view1Idx,view2Idx)
dput(mPerClV) 
}