Concordance Network NMI calculation

Description

Given a list of affinity matrices, Wall, the number of clusters, return a matrix containing the NMIs between cluster assignments made with spectral clustering.

Usage

1

Arguments

Wall

List of matrices. Each element of the list is a square, symmetric matrix that shows affinities of the data points from a certain view.

C

Number of clusters

Value

Returns an affinity matrix that represents the neighborhood graph of the data points.

Author(s)

Dr. Anna Goldenberg, Bo Wang, Aziz Mezlini, Feyyaz Demir

Examples

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# How to use SNF with multiple views

# Load views into list "dataL"
data(dataL)
data(label)

# Set the other parameters
K = 20 # number of neighbours
alpha = 0.5 # hyperparameter in affinityMatrix
T = 20 # number of iterations of SNF
# Normalize the features in each of the views.
#dataL = lapply(dataL, standardNormalization)

# Calculate the distances for each view
distL = lapply(dataL, function(x) dist2(x, x))

# Construct the similarity graphs
affinityL = lapply(distL, function(x) affinityMatrix(x, K, alpha))

# an example of how to use concordanceNetworkNMI
Concordance_matrix = concordanceNetworkNMI(affinityL, 3);

## The output, Concordance_matrix,
## shows the concordance between the fused network and each individual network.