mvc (version 1.3)

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

Description

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

Usage

consensusMeansPerClVSkm(view1, view2, view1Idx, view2Idx)

Arguments

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.

Value

Examples

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) 
}