EHC: Ensemble hierarchical clustering via graph partitioning...

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

Ensemble hierarchical clustering via graph partitioning (METIS / MST)

Description

Ensemble Hierarchical Clustering (EHC) builds a cluster association graph from the agglomerative clustering results of each data source. Strengths of association between pairs of objects are accumulated across the dendrograms (weighted by cluster depth and intra-cluster proximity), yielding a weighted adjacency matrix that is then partitioned.

Two partitioning strategies are available through graphPartitioning:

"METIS"

Delegates the partitioning of the association graph to an external graph-partitioning executable (METIS / gpmetis / shmetis, or the corresponding MATLAB hmetis routine). These binaries are not bundled with the package and must be configured through the executable argument. When executable=FALSE (the default), or when the configured executable cannot be located, the function stops with an informative error.

"MST"

A pure-R minimum spanning tree partitioning that needs no external executable. It does require the igraph package; if igraph is not installed the function stops with an informative error.

Usage

EHC(
  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,
  gap = FALSE,
  maxK = 15,
  graphPartitioning = c("METIS", "MST"),
  optimalk = 7,
  waitingtime = 300,
  file_number = 0,
  executable = FALSE
)

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. 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".
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 graph partitioning strategy. One of "METIS" (external executable) or "MST" (pure R, requires igraph). Defaults to "METIS".
optimalk	An estimate of the final optimal number of clusters. Default is 7.
waitingtime	The maximum number of seconds to wait for the external program to write its result before stopping. Defaults to 300.
file_number	An identifier appended to the temporary files exchanged with the external program. Defaults to 00.
executable	Either FALSE (the default) or the path/flag identifying the external graph-partitioning executable (METIS / gpmetis / shmetis) or MATLAB to use for the "METIS" strategy. When FALSE the "METIS" path stops, since no partition can be computed in pure R. Precompiled binaries are not bundled with the package.

Value

The returned value is a list of two elements:

DistM	The weighted cluster association matrix
Clust	The resulting clustering

The value has class 'Ensemble'.

References

Mirzaei A. and Rahmati M. (2010). A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations. IEEE Transactions on Fuzzy Systems, 18(1), 27-39.

Examples

## Not run: 
data(mosaic_toy)
L <- mosaic_toy$List

# METIS path requires an external graph-partitioning executable / MATLAB
# configured through the 'executable' argument.
res <- EHC(List = L, type = "data",
  distmeasure = c("euclidean", "euclidean"), normalize = c(FALSE, FALSE),
  method = c(NULL, NULL), clust = "agnes",
  linkage = c("flexible", "flexible"), alpha = 0.625, gap = FALSE,
  maxK = 15, graphPartitioning = "METIS", optimalk = 7,
  executable = "./MetisAlgorithm")

# MST path is pure R but needs the igraph package.
res2 <- EHC(List = L, type = "data", graphPartitioning = "MST")

## End(Not run)

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