Package {moc.gapbk}

Type:	Package
Title:	Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge
Version:	0.2.1
Description:	Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge ('MOC-GaPBK') proposed by Parraga-Alava and others (2018) <doi:10.1186/s13040-018-0178-4>. The algorithm performs gene clustering using 'NSGA-II' as the underlying multi-objective evolutionary engine, together with Path-Relinking and Pareto Local Search as intensification and diversification strategies. Two versions of the Xie-Beni validity index are used as objective functions, one per distance matrix, so that prior biological knowledge can be incorporated through the second matrix.
License:	GPL-2
Encoding:	UTF-8
Language:	en-US
Depends:	R (≥ 3.5.0)
Imports:	stats, utils, nsga2R, foreach, parallel, doParallel
Suggests:	amap, testthat (≥ 3.0.0), knitr, rmarkdown
URL:	https://github.com/jorgeklz/package-moc.gapbk
BugReports:	https://github.com/jorgeklz/package-moc.gapbk/issues
VignetteBuilder:	knitr
Config/testthat/edition:	3
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-05-13 21:10:01 UTC; jorge
Author:	Jorge Parraga-Alava [aut, cre, cph], Marcio Dorn [aut], Mario Inostroza-Ponta [aut]
Maintainer:	Jorge Parraga-Alava <jorge.parraga@utm.edu.ec>
Repository:	CRAN
Date/Publication:	2026-05-14 14:10:10 UTC

moc.gapbk: Multi-Objective Clustering Guided by a-Priori Biological Knowledge

Description

The moc.gapbk package implements the MOC-GaPBK algorithm proposed by Parraga-Alava and others (2018). It combines NSGA-II with Path-Relinking and Pareto Local Search to discover clustering solutions that are good with respect to two objective functions simultaneously, typically defined from two distance matrices: one over the data itself and one encoding a-priori biological knowledge.

Details

The main user-facing function is moc.gapbk. The legacy name moc.gabk is preserved as a deprecated alias for backward compatibility.

Author(s)

Maintainer: Jorge Parraga-Alava jorge.parraga@utm.edu.ec (ORCID) [copyright holder]

Authors:

• Jorge Parraga-Alava jorge.parraga@utm.edu.ec (ORCID) [copyright holder]

• Marcio Dorn

• Mario Inostroza-Ponta

References

J. Parraga-Alava, M. Dorn, M. Inostroza-Ponta (2018). A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies. BioData Mining. 11(1) 1-16. doi:10.1186/s13040-018-0178-4.

See Also

Useful links:

• https://github.com/jorgeklz/package-moc.gapbk

• Report bugs at https://github.com/jorgeklz/package-moc.gapbk/issues

Multi-Objective Clustering Guided by a-Priori Biological Knowledge (MOC-GaPBK)

Description

Performs the MOC-GaPBK algorithm proposed by Parraga-Alava and others (2018). It receives two distance matrices and returns a set of non-dominated clustering solutions.

Usage

moc.gapbk(
  dmatrix1,
  dmatrix2,
  num_k,
  generation = 50,
  pop_size = 10,
  rat_cross = 0.8,
  rat_muta = 0.01,
  tour_size = 2,
  neighborhood = 0.1,
  local_search = FALSE,
  cores = 2
)

moc.gabk(...)

Arguments

Details

MOC-GaPBK couples NSGA-II with Path-Relinking and Pareto Local Search. Two versions of the Xie-Beni validity index are used as objectives, one per distance matrix.

moc.gabk (note the single p) is a deprecated alias kept for backward compatibility with versions 0.1.x. New code should call moc.gapbk directly.

Value

A named list with three elements:

population

A data frame containing the final population of medoids together with the values of the two objective functions, the Pareto ranking and the crowding distance, ordered accordingly.

matrix.solutions

A data frame whose columns are clustering solutions on the Pareto front. Each row corresponds to an object and each cell to its assigned cluster.

clustering

A list of named integer vectors. Element i is the partition produced by the i-th solution on the Pareto front.

Author(s)

Jorge Parraga-Alava, Marcio Dorn, Mario Inostroza-Ponta

References

J. Parraga-Alava, M. Dorn, M. Inostroza-Ponta (2018). A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies. BioData Mining. 11(1) 1-16. doi:10.1186/s13040-018-0178-4.

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2) 182-197.

F. Glover (1997). Tabu Search and Adaptive Memory Programming - Advances, Applications and Challenges. Interfaces in Computer Science and Operations Research. 1-75.

J. Dubois-Lacoste, M. Lopez-Ibanez, T. Stutzle (2015). Anytime Pareto local search. European Journal of Operational Research, 243(2) 369-385.

Examples

set.seed(1)
x <- matrix(stats::runif(50 * 20, min = -5, max = 10),
            nrow = 50, ncol = 20)

# Two distance matrices from base R; in real applications dmatrix2
# typically encodes a-priori biological knowledge (e.g. GO semantic
# similarity). See vignette("moc-gapbk-intro") for examples using
# amap::Dist() with correlation-based distances.
dmatrix1 <- as.matrix(stats::dist(x, method = "euclidean"))
dmatrix2 <- as.matrix(stats::dist(x, method = "manhattan"))

res <- moc.gapbk(dmatrix1, dmatrix2, num_k = 3,
                 generation = 5, pop_size = 6)

head(res$matrix.solutions)