ADC: Aggregated Data Clustering
In IntClust: Integrated Data Analysis via Clustering

Description Usage Arguments Details Value Note Author(s) References Examples

In order to perform aggregated data clustering, the ADClust function was written. The data matrices are aggregated into one and hierarchical clustering is performed.

1 2	ADC(List, distmeasure = "tanimoto",normalize=FALSE,method=NULL,clust = "agnes", linkage = "ward",alpha=0.625)

`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".
`normalize`	Logical. Indicates whether to normalize the distance matrices or not. 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.
`clust`	Choice of clustering function (character). Defaults to "agnes".
`linkage`	Choice of inter group dissimilarity (character). Defaults to "ward".
`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"

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.

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.

For now, only hierarchical clustering with the agnes function is implemented.

Marijke Van Moerbeke

FODEH, J. S., BRANDT, C., LUONG, B. T., HADDAD, A., SCHULTZ, M., MURPHY, T., KRAUTHAMMER, M. (2013). Complementary Ensemble Clustering of Biomedical Data. J Biomed Inform. 46(3) pp.436-443.

data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)
MCF7_ADC=ADC(L,distmeasure="tanimoto",normalize=FALSE,method=NULL,clust="agnes",
linkage="ward",alpha=0.625)