HBGF: Hybrid Bipartite Graph Formulation

In MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

Hybrid Bipartite Graph Formulation

Description

The HBGF function implements the Hybrid Bipartite Graph Formulation, a graph-based consensus clustering method. A bipartite graph is constructed from the cluster ensemble (objects on one side, clusters on the other) and partitioned with a spectral method based on the singular value decomposition followed by k-means.

Usage

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
)

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="clust" the single source clustering is skipped. Type should be one of "data", "dist" or "clust".
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.
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	The graph partitioning method. Currently "Spec" (spectral) is supported.
optimalk	An estimate of the final optimal number of clusters. Default is 7.

Value

The returned value is a list of two elements:

DistM	A NULL object
Clust	The resulting clustering

The value has class 'Ensemble'.

References

Fern, X. Z. and Brodley, C. E. (2004). Solving cluster ensemble problems by bipartite graph partitioning. Proceedings of the Twenty-First International Conference on Machine Learning (ICML).

Examples

## Not run: 
data(mosaic_toy)
L <- mosaic_toy$List
HBGF_toy <- HBGF(List = L, type = "data",
                 distmeasure = c("euclidean", "euclidean"),
                 normalize = c(FALSE, FALSE), method = c(NULL, NULL),
                 clust = "agnes", linkage = c("ward", "ward"),
                 nrclusters = c(7, 7), graphPartitioning = "Spec", optimalk = 7)

## End(Not run)

MosaiClusteR documentation built on Oct. 25, 2026, 1:08 a.m.