IntClust (version 0.1.0)

ADC: Aggregated data clustering

Description

Aggregated Data Clustering (ADC) is a direct clustering multi-source technique. ADC merges the columns of all data sets into a single large data set on which a final clustering is performed.

Usage

ADC(List, distmeasure = "tanimoto", normalize = FALSE, method = NULL,
  clust = "agnes", linkage = "flexible", alpha = 0.625)

Arguments

List

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

distmeasure

Choice of metric for the dissimilarity matrix (character). Should be one of "tanimoto", "euclidean", "jaccard", "hamming". Defaults to "tanimoto".

normalize

Logical. Indicates whether to normalize the distance matrices or not, defaults to FALSE. 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 NULL.

clust

Choice of clustering function (character). Defaults to "agnes".

linkage

Choice of inter group dissimilarity (character). Defaults to "flexible".

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".

Value

The returned value is a list with the following three elements.

AllData

Fused data matrix of the data matrices

DistM

The distance matrix computed from the AllData element

Clust

The resulting clustering

The value has class "ADC". The Clust element will be of interest for further applications.

Details

In order to perform aggregated data clustering, the ADC function was written. A list of data matrices of the same type (continuous or binary) is required as input which are combined into a single (larger) matrix. Hierarchical clustering is performed with the agnes function and the ward link on the resulting data matrix and an applicable distance measure is indicated by the user.

References

Fodeh2013IntClust

Examples

# NOT RUN {
data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)
MCF7_ADC=ADC(List=L,distmeasure="tanimoto",normalize=FALSE,method=NULL,clust="agnes",
linkage="flexible",alpha=0.625)
# }