HBGF: Hybrid bipartite graph formulation
In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description Usage Arguments Value References Examples

Hybrid Bipartite Graph Formulation (HBGF) is a graph-based consensus multi-source clustering technique. The method builds a bipartite graph in which the two types of vertices are represented by the objects on one hand and the clusters of the partitions on the other hand. An edge is only present between an object vertex and a cluster vertex indicating that the object belongs to that cluster. The graph can be partitioned with the Spectral clustering \insertCiteNg2000IntClust.

HBGF(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 = "Spec", optimalk = 7)

`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. Default is FALSE.
`maxK`	The maximal number of clusters to investigate in the gap statistic. Default is 15.
`graphPartitioning`	A character string indicating the preferred graph partitioning algorithm. For now only spectral clustering ("Spec") is implemented. Defaults to "Spec".
`optimalk`	An estimate of the final optimal number of clusters. Default is 7.

The returned value is a list of two elements:

`DistM`	A NULL object
`Clust`	The resulting clustering

The value has class 'Ensemble'.

\insertRef

Fern2004IntClust

data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)

MCF7_HBGF=HBGF(List=L,type="data",distmeasure=c("tanimoto","tanimoto"),normalize=
c(FALSE,FALSE),method=c(NULL,NULL),clust="agnes",linkage = c("flexible",
"flexible"),nrclusters=c(7,7),gap = FALSE, maxK = 15,graphPartitioning="Spec",
optimalk=7)