Package ‘ConsensusClustering’

July 30, 2024

Type Package
Title Consensus Clustering
Version 1.5.0
Description Clustering, or cluster analysis, is a widely used technique in bioinformatics to iden-
tify groups of similar biological data points. Consensus clustering is an extension to cluster-
ing algorithms that aims to construct a robust result from those clustering features that are invari-
ant under different sources of variation. For the reference, please cite the following pa-
per: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.
License GPL (>= 3)
Encoding UTF-8
RoxygenNote 7.3.2
Imports assertthat, dplyr, igraph, cluster, mvtnorm, utils, graphics,
stats
NeedsCompilation no
Author Behnam Yousefi [aut, cre, cph]
Maintainer Behnam Yousefi <yousefi.bme@gmail.com>
Repository CRAN
Date/Publication 2024-07-30 08:00:02 UTC

Contents
adj_conv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
adj_mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
cc_cluster_count . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
cluster_relabel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
coCluster_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
connectivity_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
consensus_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
consensus_matrix_data_prtrb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
consensus_matrix_multiview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
gaussian_clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
gaussian_clusters_with_param . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1
2 adj_conv

gaussian_mixture_clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
generate_data_prtrb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
generate_gaussian_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
generate_method_prtrb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
generate_multiview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
hir_clust_from_adj_mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
indicator_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
label_similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Logit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
majority_voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
multiview_clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
multiview_cluster_gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
multiview_kmeans_gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
multiview_pam_gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
multi_cluster_gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
multi_kmeans_gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
multi_pam_gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
pam_clust_from_adj_mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
spect_clust_from_adj_mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Index 29

adj_conv Convert adjacency function to the affinity matrix

Description
Convert adjacency function to the affinity matrix

Usage
adj_conv(adj.mat, alpha = 1)

Arguments
adj.mat Adjacency matrix. The elements must be within [-1, 1].
alpha soft threshold value (see details).

Details
adj = exp(-(1-adj)^2/(2*alpha^2)) ref: Luxburg (2007), "A tutorial on spectral clustering", Stat
Comput

Value
the matrix if affinity values.
adj_mat 3

Examples
Adj_mat = rbind(c(0.0,0.9,0.0),
c(0.9,0.0,0.2),
c(0.0,0.2,0.0))
adj_conv(Adj_mat)

adj_mat Covert data matrix to adjacency matrix

Description
Covert data matrix to adjacency matrix

Usage
adj_mat(X, method = "euclidian")

Arguments
X a matrix of samples by features.
method method for distance calculation: "euclidian", "cosine", "maximum", "manhattan",
"canberra", "binary", "minkowski",

Value
calculated adjacency matrix from the data matrix using the specified methods

Examples
X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")

cc_cluster_count Count the number of clusters based on stability score.

Description
Count the number of clusters based on stability score.

Usage
cc_cluster_count(CM, plot.cdf = TRUE, plot.logit = FALSE)
4 cluster_relabel

Arguments
CM list of consensus matrices each for a specific number of clusters. It can be the
output of consensus_matrix() and multiview_consensus_matrix() func-
tions.
plot.cdf binary value to plot the cumulative distribution functions of CM (default TRUE).
plot.logit binary value to plot the logit model of cumulative distribution functions of CM
(default FALSE).

Details
Count the number of clusters given a list of consensus matrices each for a specific number of
clusters. Using different methods: "LogitScore", "PAC", "deltaA", "CMavg"

Value
results as a list: "LogitScore", "PAC", "deltaA", "CMavg", "Kopt_LogitScore", "Kopt_PAC",
"Kopt_deltaA", "Kopt_CMavg"

Examples
X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")
CM = consensus_matrix(Adj, max.cluster=3, max.itter=10)
Result = cc_cluster_count(CM, plot.cdf=FALSE)

cluster_relabel Relabeling clusters based on cluster similarities

Description
Relabeling clusters based on cluster similarities

Usage
cluster_relabel(x1, x2)

Arguments
x1 clustering vector 1 Zero elements are are considered as unclustered samples
x2 clustering vector 2 Zero elements are are considered as unclustered samples

Details
When performing performing several clustering, the cluster labels may no match with each other.
To perform maximum voting, the clustering need to be relabels based on label similarities.
coCluster_matrix 5

Value
dataframe of relabeled clusters

Examples
X = gaussian_clusters()$X
x1 = kmeans(X, 5)$cluster
x2 = kmeans(X, 5)$cluster
clusters = cluster_relabel(x1, x2)

coCluster_matrix Calculate the Co-cluster matrix for a given set of clustering results.

Description
Calculate the Co-cluster matrix for a given set of clustering results.

Usage
coCluster_matrix(X, verbos = TRUE)

Arguments
X clustering matrix of Nsamples x Nclusterings. Zero elements are are considered
as unclustered samples
verbos binary value for verbosity (default = TRUE)

Details
Co-cluster matrix or consensus matrix (CM) is a method for consensus mechanism explaned in
Monti et al. (2003).

Value
The normalized matrix of Co-cluster frequency of any pairs of samples (Nsamples x Nsamples)

Examples
Clustering = cbind(c(1,1,1,2,2,2),
c(1,1,2,1,2,2))
coCluster_matrix(Clustering, verbos = FALSE)
6 consensus_matrix

connectivity_matrix Build connectivity matrix

Description

Build connectivity matrix

Usage

connectivity_matrix(clusters)

Arguments

clusters a vector of clusterings. Zero elements mean that the sample was absent during
clustering

Details

Connectivity matrix (M) is a binary matrix N-by-N M[i,j] = 1 if sample i and j are in the same
cluster ref: Monti et al. (2003) "Consensus Clustering: A Resampling-Based Method for Class
Discovery and Visualization of Gene Expression Microarray Data", Machine Learning

Value

Connectivity matrix

Examples

con_mat = connectivity_matrix(c(1,1,1,2,2,2))

consensus_matrix Calculate consensus matrix for data perturbation consensus cluster-

ing

Description

Calculate consensus matrix for data perturbation consensus clustering

consensus_matrix 7

Usage

consensus_matrix(
X,
max.cluster = 5,
resample.ratio = 0.7,
max.itter = 100,
clustering.method = "hclust",
adj.conv = TRUE,
verbos = TRUE
)

Arguments

X adjacency matrix a Nsample x Nsample

max.cluster maximum number of clusters
resample.ratio the data ratio to use at each itteration.
max.itter maximum number of itterations at each max.cluster
clustering.method
base clustering method: c("hclust", "spectral", "pam")
adj.conv binary value to apply soft thresholding (default=TRUE)
verbos binary value for verbosity (default=TRUE)

Details

performs data perturbation consensus clustering and obtain consensus matrix Monti et al. (2003)
consensus clustering algorithm This function will be removed in the future release and is replaced
by consensus_matrix_data_prtrb()

Value

list of consensus matrices for each k

Examples

X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")
CM = consensus_matrix(Adj, max.cluster=3, max.itter=10, verbos = FALSE)
8 consensus_matrix_data_prtrb

consensus_matrix_data_prtrb
Calculate consensus matrix for data perturbation consensus cluster-
ing

Description
Calculate consensus matrix for data perturbation consensus clustering

Usage
consensus_matrix_data_prtrb(
X,
max.cluster = 5,
resample.ratio = 0.7,
max.itter = 100,
clustering.method = "hclust",
adj.conv = TRUE,
verbos = TRUE
)

Arguments
X adjacency matrix a Nsample x Nsample
max.cluster maximum number of clusters
resample.ratio the data ratio to use at each itteration.
max.itter maximum number of itterations at each max.cluster
clustering.method
base clustering method: c("hclust", "spectral", "pam")
adj.conv binary value to apply soft thresholding (default=TRUE)
verbos binary value for verbosity (default=TRUE)

Details
performs data perturbation consensus clustering and obtain consensus matrix Monti et al. (2003)
consensus clustering algorithm

Value
list of consensus matrices for each k

Examples
X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")
CM = consensus_matrix_data_prtrb(Adj, max.cluster=3, max.itter=10, verbos = FALSE)
consensus_matrix_multiview 9

consensus_matrix_multiview
Calculate consensus matrix for multi-data consensus clustering

Description
Calculate consensus matrix for multi-data consensus clustering

Usage
consensus_matrix_multiview(
X,
max.cluster = 5,
sample.set = NA,
clustering.method = "hclust",
adj.conv = TRUE,
verbos = TRUE
)

Arguments
X list of adjacency matrices for different cohorts (or views).
max.cluster maximum number of clusters
sample.set vector of samples the clustering is being applied on. sample.set can be names
or indices. if sample.set is NA, it considers that all the datasets have the same
samples with the same order.
clustering.method
base clustering method: c("hclust", "spectral", "pam")
adj.conv binary value to apply soft threshold (default=TRUE)
verbos binary value for verbosity (default=TRUE)

Details
performs multi-data consensus clustering and obtain consensus matrix Monti et al. (2003) consen-
sus clustering algorithm

Value
description list of consensus matrices for each k

Examples
data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),
sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
Adj = list()
10 gaussian_clusters

for (i in 1:length(X_observation))
Adj[[i]] = adj_mat(X_observation[[i]], method = "euclidian")
CM = consensus_matrix_multiview(Adj, max.cluster = 4, verbos = FALSE)

gaussian_clusters Generate clusters of data points from Gaussian distribution with ran-
domly generated parameters

Description
Generate clusters of data points from Gaussian distribution with randomly generated parameters

Usage
gaussian_clusters(
n = c(50, 50),
dim = 2,
sd.max = 0.1,
sd.noise = 0.01,
r.range = c(0.1, 1)
)

Arguments
n vector of number of data points in each cluster The length of n should be equal
to the number of clusters.
dim number of dimensions
sd.max maximum standard deviation of clusters
sd.noise standard deviation of the added noise
r.range the range (min, max) of distance of cluster centers from the origin

Value
a list of data points (X) and cluster labels (class)

Examples
data = gaussian_clusters()
X = data$X
y = data$class
gaussian_clusters_with_param 11

gaussian_clusters_with_param
Generate clusters of data points from Gaussian distribution with given
parameters

Description
Generate clusters of data points from Gaussian distribution with given parameters

Usage
gaussian_clusters_with_param(n, center, sigma)

Arguments
n vector of number of data points in each cluster The length of n should be equal
to the number of clusters.
center matrix of centers Ncluster x dim
sigma list of covariance matrices dim X dim. The length of sigma should be equal to
the number of clusters.

Value
matrix of Nsamples x (dim + 1). The last column is cluster labels.

Examples
center = rbind(c(0,0),
c(1,1))
sigma = list(diag(c(1,1)),
diag(2,2))
gaussian_clusters_with_param(c(10, 10), center, sigma)

gaussian_mixture_clusters
Generate clusters of data points from Gaussian-mixture-model distri-
butions with randomly generated parameters

Description
Generate clusters of data points from Gaussian-mixture-model distributions with randomly gener-
ated parameters
12 generate_data_prtrb

Usage

gaussian_mixture_clusters(
n = c(50, 50),
dim = 2,
sd.max = 0.1,
sd.noise = 0.01,
r.range = c(0.1, 1),
mixture.range = c(1, 4),
mixture.sep = 0.5
)

Arguments

n vector of number of data points in each cluster The length of n should be equal
to the number of clusters.
dim number of dimensions
sd.max maximum standard deviation of clusters
sd.noise standard deviation of the added noise
r.range the range (min, max) of distance of cluster centers from the origin
mixture.range range (min, max) of the number of Gaussian-mixtures.
mixture.sep scaler indicating the separability between the mixtures.

Value

a list of data points (X) and cluster labels (class)

Examples

data = gaussian_mixture_clusters()
X = data$X
y = data$class

generate_data_prtrb Generation mechanism for data perturbation consensus clustering

Description

Generation mechanism for data perturbation consensus clustering

generate_data_prtrb 13

Usage

generate_data_prtrb(
X,
cluster.method = "pam",
k = 3,
resample.ratio = 0.7,
rep = 10,
distance.method = "euclidian",
adj.conv = TRUE,
func
)

Arguments

X input data Nsample x Nfeatures

cluster.method base clustering method: c("hclust", "spectral", "pam", "custom")
k number of clusters
resample.ratio the data ratio to use at each itteration.
rep maximum number of itterations at each max.cluster
distance.method
method for distance calculation: "euclidian", "cosine", "maximum", "manhattan",
"canberra", "binary", "minkowski".
adj.conv binary value to apply soft thresholding (default=TRUE)
func user-definrd function required if cluster.method = "custom". The function
needs two inputs of X and k

Details

Performs clustering on the purturbed samples set Monti et al. (2003) consensus clustering algorithm

Value

matrix of clusterings Nsample x Nrepeat

Examples

X = gaussian_clusters()$X
Clusters = generate_data_prtrb(X)
14 generate_method_prtrb

generate_gaussian_data
Generate a set of data points from Gaussian distribution

Description
Generate a set of data points from Gaussian distribution

Usage
generate_gaussian_data(n, center = 0, sigma = 1, label = NA)

Arguments
n number of generated data points
center data center of desired dimension
sigma covariance matrix
label cluster label

Value
Generated data points from Gaussian distribution with given parameters

Examples
generate_gaussian_data(10, center=c(0,0), sigma=diag(c(1,1)), label=1)

generate_method_prtrb Multiple method generation

Description
Multiple method generation

Usage
generate_method_prtrb(
X,
cluster.method = "pam",
range.k = c(2, 5),
sample.k.method = "random",
rep = 10,
distance.method = "euclidian",
func
)
generate_multiview 15

Arguments
X input data Nsample x Nfeatures
cluster.method base clustering method: c("kmeans", "pam", "custom")
range.k vector of minimum and maximum values for k c(min, max)
sample.k.method
method for the choice of k at each repeat c("random", "silhouette")
rep number of repeats
distance.method
method for distance calculation: "euclidian", "maximum", "manhattan", "canberra",
"binary", "minkowski".
func user-definrd function required if cluster.method = "custom". The function
needs two inputs of X and k.

Details
At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform
distribution between range.k[1] and range.k[2]. Then clustering is applied and result is returned.

Value
matrix of clusterings Nsample x Nrepeat

Examples
X = gaussian_clusters()$X
Clusters = generate_method_prtrb(X)

generate_multiview Multiview generation

Description
Multiview generation

Usage
generate_multiview(
X,
cluster.method = "pam",
range.k = c(2, 5),
sample.k.method = "random",
rep = 10,
distance.method = "euclidian",
sample.set = NA,
func
)
16 hir_clust_from_adj_mat

Arguments

X list of input data matrices of Sample x feature or distance matrices. The length
of X is equal to Nviews
cluster.method base clustering method: c("kmeans", "pam", "custom")
range.k vector of minimum and maximum values for k c(min, max)
sample.k.method
method for the choice of k at each repeat c("random", "silhouette")
rep number of repeats
distance.method
method for distance calculation: "euclidian", "maximum", "manhattan", "canberra",
"binary", "minkowski".
sample.set vector of samples the clustering is being applied on. can be names or indices. If
sample.set is NA, it considers all the datasets have the same samples with the
same order
func user-definrd function required if cluster.method = "custom". The function
needs two inputs of X and k.

Details

At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform
distribution between range.k[1] and range.k[2]. Then clustering is applied and result is returned.

Value

matrix of clusterings Nsample x Nrepeat

Examples
data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),
sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
Clusters = multiview_pam_gen(X_observation)

hir_clust_from_adj_mat
Hierarchical clustering from adjacency matrix

Description

Hierarchical clustering from adjacency matrix

indicator_matrix 17

Usage
hir_clust_from_adj_mat(
adj.mat,
k = 2,
alpha = 1,
adj.conv = TRUE,
method = "ward.D"
)

Arguments
adj.mat adjacency matrix
k number of clusters (default=2)
alpha soft threshold (considered if adj.conv = TRUE) (default=1)
adj.conv binary value to apply soft thresholding (default=TRUE)
method distance method (default: ward.D)

Details
apply PAM (k-medoids) clustering on the adjacency matrix

Value
vector of clusters

Examples
Adj_mat = rbind(c(0.0,0.9,0.0),
c(0.9,0.0,0.2),
c(0.0,0.2,0.0))
hir_clust_from_adj_mat(Adj_mat)

indicator_matrix Build indicator matrix

Description
Build indicator matrix

Usage
indicator_matrix(clusters)

Arguments
clusters a vector of clusterings. Zero elements mean that the sample was absent during
clustering
18 label_similarity

Details
Indicator matrix (I) is a binary matrix N-by-N I[i,j] = 1 if sample i and j co-exist for clustering ref:
Monti et al. (2003) "Consensus Clustering: A Resampling-Based Method for Class Discovery and
Visualization of Gene Expression Microarray Data", Machine Learning

Value
Indicator matrix

Examples
ind_mat = indicator_matrix(c(1,1,1,0,0,1))

label_similarity Similarity between different clusters

Description
Similarity between different clusters

Usage
label_similarity(x1, x2)

Arguments
x1 clustering vector 1 Zero elements are are considered as unclustered samples
x2 clustering vector 2 Zero elements are are considered as unclustered samples

Details
When performing several clustering, the cluster labels may not match with each other. To find
correspondences between clusters, the similarity between different labels will be calculated.

Value
matrix of similarities between clustering labels

Examples
X = gaussian_clusters()$X
x1 = kmeans(X, 5)$cluster
x2 = kmeans(X, 5)$cluster
Sim = label_similarity(x1, x2)
Logit 19

Logit Logit function

Description
Logit function

Usage
Logit(x)

Arguments
x numerical scaler input

Value
Logit(x) = log(1*x/(1-x))

Examples
y = Logit(0.5)

majority_voting Consensus mechanism based on majority voting

Description
Consensus mechanism based on majority voting

Usage
majority_voting(X)

Arguments
X clustering matrix of Nsamples x Nclusterings. Zero elements are are considered
as unclustered samples

Details
Perform majority voting as a consensus mechanism.

Value
the vector of consensus clustering result
20 multiview_clusters

Examples
X = gaussian_clusters()$X
x1 = kmeans(X, 5)$cluster
x2 = kmeans(X, 5)$cluster
x3 = kmeans(X, 5)$cluster
clusters = majority_voting(cbind(x1,x2,x3))

multiview_clusters Generate multiview clusters from Gaussian distributions with ran-

domly generated parameters

Description
Generate multiview clusters from Gaussian distributions with randomly generated parameters

Usage
multiview_clusters(
n = c(50, 50),
hidden.dim = 2,
observed.dim = c(2, 2, 3),
sd.max = 0.1,
sd.noise = 0.01,
hidden.r.range = c(0.1, 1)
)

Arguments
n vector of number of data points in each cluster The length of n should be equal
to the number of clusters.
hidden.dim scaler value of dimensions of the hidden state
observed.dim vector of number of dimensions of the generate clusters. The length of observed.dim
should be equal to the number of clusters.
sd.max maximum standard deviation of clusters
sd.noise standard deviation of the added noise
hidden.r.range the range (min, max) of distance of cluster centers from the origin in the hidden
space.

Value
a list of data points (X) and cluster labels (class)

Examples
data = multiview_clusters()
multiview_cluster_gen 21

multiview_cluster_gen Multiview cluster generation

Description
Multiview cluster generation

Usage
multiview_cluster_gen(
X,
func,
rep = 10,
param,
is.distance = FALSE,
sample.set = NA
)

Arguments
X List of input data matrices of Sample x feature or distance matrices. The length
of X is equal to Nviews
func custom function that accepts X and a parameter that return a vector of clusterings.
cluster_func <- function(X, param)
rep number of repeats
param vector of parameters
is.distance binary balue indicating if the input X[i] is distance
sample.set vector of samples the clustering is being applied on. can be names or indices. if
sample.set is NA, it considers all the datasets have the same samples with the
same order

Details
At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform
distribution between range.k[1] and range.k[2]. Then clustering is applied and result is returned.

Value
matrix of clusterings Nsample x (Nrepeat x Nviews)

Examples
data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),
sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
cluster_func = function(X,rep,param){return(multi_kmeans_gen(X,rep=rep,range.k=param))}
Clusters = multiview_cluster_gen(X_observation, func = cluster_func, rep = 10, param = c(2,4))
22 multiview_kmeans_gen

multiview_kmeans_gen Multiview K-means generation

Description

Multiview K-means generation

Usage

multiview_kmeans_gen(X, rep = 10, range.k = c(2, 5), method = "random")

Arguments

X List of input data matrices of Sample x feature. The length of X is equal to

Nviews
rep number of repeats
range.k vector of minimum and maximum values for k c(min, max)
method method for the choice of k at each repeat c("random", "silhouette")

Details

At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform
distribution between range.k[1] and range.k[2]. Then k-means clustering is applied and result is
returned.

Value

matrix of clusterings Nsample x (Nrepeat x Nviews)

Examples

data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),

sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
Clusters = multiview_kmeans_gen(X_observation)
multiview_pam_gen 23

multiview_pam_gen Multiview PAM (K-medoids) generation

Description
Multiview PAM (K-medoids) generation

Usage
multiview_pam_gen(
X,
rep = 10,
range.k = c(2, 5),
is.distance = FALSE,
method = "random",
sample.set = NA
)

Arguments
X List of input data matrices of Sample x feature or distance matrices. The length
of X is equal to Nviews
rep number of repeats
range.k vector of minimum and maximum values for k c(min, max)
is.distance binary balue indicating if the input X is distance
method method for the choice of k at each repeat c("random", "silhouette")
sample.set vector of samples the clustering is being applied on. can be names or indices. if
sample.set is NA, it considers all the datasets have the same samples with the
same order

Details
At each repeat, k is selected randomly or based on the best silhouette width from a discrete uni-
form distribution between range.k[1] and range.k[2]. Then PAM clustering is applied and result is
returned.

Value
matrix of clusterings Nsample x (Nrepeat x Nviews)

Examples
data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),
sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
Clusters = multiview_pam_gen(X_observation)
24 multi_cluster_gen

multi_cluster_gen Multiple cluster generation

Description

Multiple cluster generation

Usage

multi_cluster_gen(X, func, rep = 10, param, method = "random")

Arguments

X input data Nsample x Nfeatures or a distance matrix

func custom function that accepts X and a parameter that return a vector of clusterings.
cluster_func <- function(X, param)
rep number of repeats
param vector of parameters
method method for the choice of k at each repeat c("random", "silhouette")

Details

At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform
distribution between range.k[1] and range.k[2]. Then clustering is applied and result is returned.

Value

matrix of clusterings Nsample x Nrepeat

Examples

X = gaussian_clusters()$X
cluster_func = function(X, k){return(stats::kmeans(X, k)$cluster)}
Clusters = multi_cluster_gen(X, cluster_func, param = c(2,3))
multi_kmeans_gen 25

multi_kmeans_gen Multiple K-means generation

Description

Multiple K-means generation

Usage

multi_kmeans_gen(X, rep = 10, range.k = c(2, 5), method = "random")

Arguments

X input data Nsample x Nfeatures

rep number of repeats
range.k vector of minimum and maximum values for k c(min, max)
method method for the choice of k at each repeat c("random", "silhouette")

Details

At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform
distribution between range.k[1] and range.k[2]. Then k-means clustering is applied and result is
returned.

Value

matrix of clusterings Nsample x Nrepeat

Examples
X = gaussian_clusters()$X
Clusters = multi_kmeans_gen(X)

multi_pam_gen Multiple PAM (K-medoids) generation

Description

Multiple PAM (K-medoids) generation

26 pam_clust_from_adj_mat

Usage
multi_pam_gen(
X,
rep = 10,
range.k = c(2, 5),
is.distance = FALSE,
method = "random"
)

Arguments
X input data Nsample x Nfeatures or distance matrix.
rep number of repeats
range.k vector of minimum and maximum values for k c(min, max)
is.distance binary balue indicating if the input X is distance
method method for the choice of k at each repeat c("random", "silhouette")

Details
At each repeat, k is selected randomly or based on the best silhouette width from a discrete uni-
form distribution between range.k[1] and range.k[2]. Then PAM clustering is applied and result is
returned.

Value
matrix of clusterings Nsample x Nrepeat

Examples
X = gaussian_clusters()$X
Clusters = multi_pam_gen(X)

pam_clust_from_adj_mat
PAM (k-medoids) clustering from adjacency matrix

Description
PAM (k-medoids) clustering from adjacency matrix

Usage
pam_clust_from_adj_mat(adj.mat, k = 2, alpha = 1, adj.conv = TRUE)
spect_clust_from_adj_mat 27

Arguments

adj.mat adjacency matrix

k number of clusters (default=2)
alpha soft threshold (considered if adj.conv = TRUE) (default=1)
adj.conv binary value to apply soft thresholding (default=TRUE)

Details

apply PAM (k-medoids) clustering on the adjacency matrix

Value

vector of clusters

Examples

Adj_mat = rbind(c(0.0,0.9,0.0),
c(0.9,0.0,0.2),
c(0.0,0.2,0.0))
pam_clust_from_adj_mat(Adj_mat)

spect_clust_from_adj_mat
Spectral clustering from adjacency matrix

Description

Spectral clustering from adjacency matrix

Usage

spect_clust_from_adj_mat(
adj.mat,
k = 2,
max.eig = 10,
alpha = 1,
adj.conv = TRUE,
do.plot = FALSE
)
28 spect_clust_from_adj_mat

Arguments
adj.mat adjacency matrix
k number of clusters (default=2)
max.eig maximum number of eigenvectors in use (dafaut = 10).
alpha soft threshold (considered if adj.conv = TRUE) (default = 1)
adj.conv binary value to apply soft thresholding (default = TRUE)
do.plot binary value to do plot (dafaut = FALSE)

Details
apply PAM (k-medoids) clustering on the adjacency matrix

Value
vector of clusters

Examples
Adj_mat = rbind(c(0.0,0.9,0.0),
c(0.9,0.0,0.2),
c(0.0,0.2,0.0))
hir_clust_from_adj_mat(Adj_mat)
Index

adj_conv, 2
adj_mat, 3

cc_cluster_count, 3
cluster_relabel, 4
coCluster_matrix, 5
connectivity_matrix, 6
consensus_matrix, 6
consensus_matrix_data_prtrb, 8
consensus_matrix_multiview, 9

gaussian_clusters, 10
gaussian_clusters_with_param, 11
gaussian_mixture_clusters, 11
generate_data_prtrb, 12
generate_gaussian_data, 14
generate_method_prtrb, 14
generate_multiview, 15

hir_clust_from_adj_mat, 16

indicator_matrix, 17

label_similarity, 18
Logit, 19

majority_voting, 19
multi_cluster_gen, 24
multi_kmeans_gen, 25
multi_pam_gen, 25
multiview_cluster_gen, 21
multiview_clusters, 20
multiview_kmeans_gen, 22
multiview_pam_gen, 23

pam_clust_from_adj_mat, 26

spect_clust_from_adj_mat, 27

29