acca: Average correlation clustering algorithm

In meantrix/corrP: corrp (Compute correlations type analysis in parallel)

Average correlation clustering algorithm

Description

A C++ implementation of the ACCA method that works directly with the correlation matrix derived from the corr_matrix function. In this sense, this implementation differs from the original, it works with mixed data and several correlation methods.

Usage

acca(m, ...)

## S3 method for class 'cmatrix'
acca(m, k, maxrep = 2L, maxiter = 100L, ...)

## S3 method for class 'matrix'
acca(m, k, maxrep = 2L, maxiter = 100L, ...)

Arguments

m	\[matrix(1)] correlation matrix from corr_matrix or a distance matrix.
...	Additional arguments (TODO).
k	\[integer(1)] number of clusters considered.
maxrep	\[integer(1)] maximum number of interactions without change in the clusters.
maxiter	\[integer(1)] maximum number of interactions.

Value

\[acca_list(k)]
A list with the final result of the clustering method. That is, the name of the variables belonging to each cluster k.

Author(s)

Igor D.S. Siciliani

References

Bhattacharya, Anindya, and Rajat K. De. "Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values." Journal of Biomedical Informatics 43.4 (2010): 560-568.

Examples

## Not run: 

x = corrp::corrp(iris)
m = corrp::corr_matrix(x)
corrp::acca(m,2)


## End(Not run)

meantrix/corrP documentation built on Oct. 22, 2024, 10:16 a.m.