IntClust (version 0.1.0)

CVAA: Cumulative voting-based aggregation algorithm

Description

The CVAA includes the ensemble clustering methods CVAA and W-CVAA which are voting-based consensus methods.

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

The reference structure to be updated.

nrclustersR

The number of clusters present in the reference structure. Default is 7.

List

A list of data matrices. It is assumed the rows are corresponding with the objects.

typeL

indicates whether the provided matrices in "List" are either data matrices, distance matrices or clustering results obtained from the data. If type="dist" the calculation of the distance matrices is skipped and if type="clusters" the single source clustering is skipped. Type should be one of "data", "dist" or "clusters".

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. This is recommended if different distance types are used. 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 method to be performed: "CVAA","W-CVAA".

optimalk

An estimate of the final optimal number of clusters. Default is nrclustersR.

Value

The returned value is a list of two elements:

DistM

A NULL object

Clust

The resulting clustering

The value has class 'Ensemble'.

Details

Saeed2012IntClust describe the Cumulative Voting-based Aggregation Algorithm (CVAA) and introduce a variation Weighted Cumulative Voting-based Aggregation Algorithm (W-CVAA, Saeed2014IntClust). In the CVAA algorithm, one data partitioning is chosen as the reference partition. In a first step each partition is relabelled with respect to the reference partition in search of an optimal relabelling. In the next step a consensus partition is obtained. The W-CVAA algorithm is similar but appoints weights to each individual partition. The weights are based on the mutual information of the partition measured by the Shannon entropy.

References

Saeed2012IntClust Saeed2014IntClust

Examples

# NOT RUN {
data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)

MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)

MCF7_CVAA=CVAA(Reference=MCF7_T,nrclustersR=7,List=L,typeL="data",
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="CVAA",optimalk=7)
# }