% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/voting.r
\name{EvidenceAccumulation}
\alias{EvidenceAccumulation}
\title{Evidence accumulation clustering (co-association)}
\usage{
EvidenceAccumulation(
  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 = c("MST", "SL", "SL_agnes"),
  t = NULL
)
}
\arguments{
\item{List}{A list of data matrices. It is assumed the rows correspond with the objects.}

\item{type}{Indicates whether the provided matrices in "List" are either data
matrices ("data"), distance matrices ("dist") or clustering results ("clust").}

\item{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").}

\item{normalize}{Logical. Indicates whether to normalize the distance matrices or not, defaults to c(FALSE, FALSE).}

\item{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).}

\item{clust}{Choice of clustering function (character). Defaults to "agnes".}

\item{linkage}{Choice of inter group dissimilarity (character) for each data set. Defaults to c("flexible","flexible").}

\item{alpha}{The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625.}

\item{nrclusters}{The number of clusters to divide each individual dendrogram in. Default is c(7,7).}

\item{gap}{Logical. Whether the optimal number of clusters should be determined with the gap statistic. Defaults to FALSE.}

\item{maxK}{The maximal number of clusters to investigate in the gap statistic. Default is 15.}

\item{graphPartitioning}{The graph partitioning method to be performed: "MST", "SL" or "SL_agnes".}

\item{t}{A threshold used by the "MST" and "SL" partitioning procedures. Defaults to NULL.}
}
\value{
A list of two elements:
\item{DistM}{A NULL object}
\item{Clust}{The resulting clustering}
The value has class 'Ensemble' (or 'Single Clustering' for "SL_agnes").
}
\description{
\code{EvidenceAccumulation} builds a co-association
(similarity) matrix from the individual source partitions and extracts a
consensus partition by graph partitioning. Three graph-partitioning options
are available: a minimum-spanning-tree procedure (\code{"MST"}, requires the
\pkg{igraph} package), a thresholded single-link procedure (\code{"SL"}), and
an agnes single-link procedure (\code{"SL_agnes"}).
}
\details{
The co-association matrix \code{S} records the fraction of source
partitions in which two objects are co-clustered. \code{"MST"} prunes a
minimum spanning tree of the similarity graph (optionally at threshold
\code{t}); \code{"SL"} performs a thresholded single-link grouping; and
\code{"SL_agnes"} runs agnes single linkage on the dissimilarity \code{1-S}.
The \code{"MST"} path requires the suggested package \pkg{igraph}.
}
\examples{
\dontrun{
data(mosaic_toy)
L <- mosaic_toy$List
res <- EvidenceAccumulation(List = L, type = "data",
  distmeasure = c("euclidean","euclidean"), nrclusters = c(5,5),
  graphPartitioning = "SL_agnes")
}
}
\references{
Fred A.L.N. and Jain A.K. (2005). Combining multiple clusterings
using evidence accumulation. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 27(6), 835-850.
}
