IntClust (version 0.1.0)

EvidenceAccumulation: Evidence accumulation

Description

The Evidence Accumulation (EA, Fred2005IntClust) sets up the same similarity matrix and applies a minimum spanning tree (MST,Prim1957IntClust) algorithm to the corresponding graph. The algorithm will connect all objects with the shortest past. In order to recover the clusters, weak edges are cut at a threshold value $t$.

Usage

EvidenceAccumulation(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, graphPartitioning = c("MTS", "SL", "SL_agnes"), t = NULL)

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, 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.

graphPartitioning

The method to be performed: "MTS", "SL", "SL_agnes".

t

A threshold to cut weak edges. Default is NULL.

Value

The returned value is a list of two elements:

DistM

A NULL object

Clust

The resulting clustering

The value has class 'Ensemble'.

References

Fred2005IntClust Prim1957IntClust

Examples

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

MCF7_EA=EvidenceAccumulation(List=L,type="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,graphPartitioning="MTS")
# }