ConsensusClustering: Voting-based consensus clustering

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

Voting-based consensus clustering

Description

Consensus Clustering performs voting-based consensus on a set of single-source partitions. Each data matrix is clustered separately and the resulting label matrix is reconciled into a single consensus partition by an iterative voting scheme. Three voting methods are available: Iterative Voting Consensus (IVC), Iterative Probabilistic Voting Consensus (IPVC) and Iterative Pairwise Consensus (IPC).

Usage

ConsensusClustering(
  List,
  type = 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("IVC", "IPVC", "IPC"),
  optimalk = 7
)

Arguments

List	A list of data matrices. It is assumed the rows are corresponding with the objects.
type	Indicates whether the provided matrices in "List" are either data matrices ("data"), distance matrices ("dist") or clustering results obtained from the data ("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) for two data sets. More details on normalization in Normalization.
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) for two data sets.
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") for two data sets.
alpha	The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625 and is only used if the linkage is set to "flexible".
nrclusters	The number of clusters to divide each individual dendrogram in. Default is c(7,7) for two data sets.
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 voting method to be performed: one of "IVC", "IPVC" or "IPC".
optimalk	The number of clusters for the final consensus partition. Defaults to 7.

Value

The returned value is a list of two elements:

DistM	A NULL object
Clust	The resulting clustering

The value has class 'Ensemble'.

References

Nguyen N. and Caruana R. (2007). Consensus Clusterings. Seventh IEEE International Conference on Data Mining (ICDM 2007), 607-612.

Examples

## Not run: 
data(mosaic_toy)
fit <- ConsensusClustering(List = mosaic_toy$List, type = "data",
  distmeasure = c("euclidean", "euclidean"), normalize = c(FALSE, FALSE),
  method = c(NULL, NULL), clust = "agnes", linkage = c("ward", "ward"),
  nrclusters = c(3, 3), gap = FALSE, votingMethod = "IVC", optimalk = 3)

## End(Not run)

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