ADEC: Aggregated Data Ensemble Clustering
In IntClust: Integrated Data Analysis via Clustering

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

Function ADEC performs which the functions ADECa, ADECb and ADECc is specified by the user.

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ADEC(List, distmeasure = "tanimoto",normalize=FALSE,method=NULL, t = 10,
r = NULL, nrclusters = NULL, clust = "agnes", linkage = "ward",alpha=0.625
,ResampleFeatures=TRUE)

`List`	A list of data matrices of the same type. It is assumed the rows are corresponding with the objects.
`distmeasure`	The distance measure to be used on the fused data 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.
`t`	The number of iterations.
`r`	Optional. The number of features to take for the random sample.
`nrclusters`	The number of clusters to cut the dendrogram in. If a sequence is specified either ADECb or ADECc is performed. A fixed number of clusters defaults to ADECa
`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"
`ResampleFeatures`	Logical. Whether the features should be resamples. If TRUE, either ADECa or ADECc is performed.

See the details of ADECa, ADECb and ADEDc for more information.

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

`AllData`	Fused data matrix of the data matrices
`S`	The resulting co-association matrix
`Clust`	The resulting clustering

The value has class 'ADEC'. The Clust element will be of interest for further applications.

For now, only hierarchical clustering with the agnes function 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.

ADECa, ADECb,ADECc

data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)
MCF7_ADECa=ADEC(L,distmeasure="tanimoto",normalize=FALSE,method=NULL,t=25,r=NULL,
nrclusters=7,clust="agnes",linkage="ward",alpha=0.625,ResampleFeatures=TRUE)