CVAA: Cumulative Voting Aggregation (CVAA / W-CVAA)

In MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

Cumulative Voting Aggregation (CVAA / W-CVAA)

Description

CVAA performs cumulative voting consensus clustering. A reference partition is used to align the individual source partitions, which are then aggregated into a consensus stochastic membership matrix. Two variants are available: cumulative voting ("CVAA") and the information-weighted variant ("W-CVAA").

Usage

CVAA(
  Reference = NULL,
  nrclustersR = 7,
  List,
  typeL = c("data", "dist", "clust"),
  distmeasure = c("tanimoto", "tanimoto"),
  normalize = c(FALSE, FALSE),
  method = c(NULL, NULL),
  clust = "agnes",
  linkage = c("flexible", "flexible"),
  alpha = 0.625,
  nrclusters = c(7, 7),
  gap = FALSE,
  maxK = 15,
  votingMethod = c("CVAA", "W-CVAA"),
  optimalk = nrclustersR
)

Arguments

Reference	A reference partition (a clustering result, optionally an object produced by a weighted/CEC method). Required.
nrclustersR	The number of clusters of the reference partition. Default is 7.
List	A list of data matrices. It is assumed the rows correspond with the objects.
typeL	Indicates whether the provided matrices in "List" are either data matrices ("data"), distance matrices ("dist") or clustering results ("clust").
distmeasure	A vector of the distance measures to be used on each data matrix. Should be one of "tanimoto", "euclidean", "jaccard", "hamming". Defaults to c("tanimoto","tanimoto").
normalize	Logical. Indicates whether to normalize the distance matrices or not, defaults to c(FALSE, FALSE).
method	A method of normalization. Should be one of "Quantile","Fisher-Yates", "standardize","Range" or any of the first letters of these names. Default is c(NULL,NULL).
clust	Choice of clustering function (character). Defaults to "agnes".
linkage	Choice of inter group dissimilarity (character) for each data set. Defaults to c("flexible","flexible").
alpha	The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625.
nrclusters	The number of clusters to divide each individual dendrogram in. Default is c(7,7).
gap	Logical. Whether the optimal number of clusters should be determined with the gap statistic. Defaults to FALSE.
maxK	The maximal number of clusters to investigate in the gap statistic. Default is 15.
votingMethod	The method to be performed: "CVAA" or "W-CVAA".
optimalk	An estimate of the final optimal number of clusters. Defaults to nrclustersR.

Details

Cumulative voting aggregation aligns each source membership matrix to a running consensus by least-squares voting, then averages cumulatively. The weighted variant orders the partitions by average information content and weights their contribution accordingly. When optimalk is smaller than nrclustersR the JS-ALink agglomeration is used to merge reference clusters.

Value

A list of two elements:

DistM	A NULL object
Clust	The resulting clustering (order, order.lab and Clusters)

The value has class 'Ensemble'.

References

Ayad H.G. and Kamel M.S. (2008). Cumulative voting consensus method for partitions with variable number of clusters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1), 160-173.

Examples

## Not run: 
data(mosaic_toy)
L <- mosaic_toy$List
Ref <- Cluster(L[[1]], type = "data", distmeasure = "euclidean")
attr(Ref, "method") <- "Single"
res <- CVAA(Reference = Ref, nrclustersR = 5, List = L, typeL = "data",
  distmeasure = c("euclidean","euclidean"), votingMethod = "CVAA", optimalk = 5)

## End(Not run)

MosaiClusteR documentation built on Oct. 25, 2026, 1:08 a.m.