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Type:

Package

Title:

Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge

Version:

0.1.3

Date:

2024-08-29

Author:

Jorge Parraga-Alava

[aut, cre]

Maintainer:

Jorge Parraga-Alava <jorge.parraga@usach.cl>

Description:

Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) which was proposed by Parraga-Alava, J. et. al. (2018) <doi:10.1186/s13040-018-0178-4>.

Depends:

R (≥ 3.2.5)

License:

GPL-2

Encoding:

UTF-8

Imports:

stats, amap, nsga2R, foreach, parallel, doParallel, utils, doSNOW, doMPI

RoxygenNote:

7.2.3

Suggests:

knitr, rmarkdown

NeedsCompilation:

Packaged:

2024-08-29 16:13:18 UTC; UTM

Repository:

CRAN

Date/Publication:

2024-08-29 16:50:02 UTC

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

Description

This function receives two distance matrices and it performs the MOC-GaPBK.

Usage

moc.gabk(
  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
)

Arguments

dmatrix1

A distance matrix. It should have the same dimensions that dmatrix2. It is mandatory.

dmatrix2

A distance matrix. It should have the same dimensions that dmatrix1. It is mandatory.

num_k

The number k of groups represented by medoids in each individual. It is mandatory.

generation

Number of generations to be performed by MOC-GaPBK. By default 50.

pop_size

Size of population. By default 10.

rat_cross

Probability of crossover. By default 0.80.

rat_muta

Probability of mutation. By default 0.01.

tour_size

Size of tournament. By default 2.

neighborhood

Percentage of neighborhood. A real value between 0 and 1. It is computed as neighborhood*pop_size to determine the size of neighborhood. By default 0.10.

local_search

A boolean value indicating whether the local searches procedures (PR and PLS) are computed. By default FALSE.

cores

Number of cores to be used to compute the local searches procedures. By default 2.

Details

MOC-GaPBK is a method proposes by Parraga-Alava, J. et. al. 2018. It carries out the discovery of clusters using NSGA-II algorithm along with Path-Relinking (PR) and Pareto Local Search (PLS) as intensification and diversification strategies, respectively. The algorithm uses as objective functions two versions of the Xie-Beni validity index, i.e., a version for each distance matrix (dmatrix1, dmatrix2). More details about this compute can be found in: <https://doi.org/10.1186/s13040-018-0178-4>. MOC-GaPBK yield a set of the best clustering solutions from a multi-objective point of views.

Value

population

The population of medoids including the objective functions values and order by Pareto ranking and crowding distance values.

matrix.solutions

A matrix with results of clustering. Each column represents a clustering solution available in Pareto front.

clustering

A list containing named vectors of integers from 1:k representing the cluster to which each object is assigned.

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.

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: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies. 1-75.

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

Examples


##Generates a data matrix of dimension 50X20

library("amap")
library("moc.gapbk")

x <- matrix(runif(50 * 20, min = -5, max = 10), nrow = 50, ncol = 20)

##Compute two distance matrices

dmatrix1<- as.matrix(amap::Dist(x, method = "euclidean"))
dmatrix2<- as.matrix(amap::Dist(x, method = "correlation"))

##Performs MOC-GaPBK with 5 cluster

example<-moc.gabk(dmatrix1, dmatrix2, 5)

example$population
example$matrix.solutions
example$clustering