# pop: Optimal Partition (classification). In amap: Another Multidimensional Analysis Package

## Description

Classification: Computing an Optimal Partition from Weighted Categorical Variables or from an Array of Signed Similarities.

## Usage

 1 pop(x,fmbvr=TRUE,triabs=TRUE,allsol=TRUE)

## Arguments

 x A dissimilarity matrix fmbvr Logical, TRUE: look for the exact solution triabs Logical, TRUE: try to init with absolute values allsol Logical, TRUE all solutions, FALSE only one solution

## Author(s)

Michel Petitjean, http://petitjeanmichel.free.fr/itoweb.petitjean.class.html

R port by Antoine Lucas, http://mulcyber.toulouse.inra.fr/projects/amap/

## References

Theory is explained at http://petitjeanmichel.free.fr/itoweb.petitjean.class.html

Marcotorchino F. Agr\'egation des similarit\'es en classification automatique. Th\'ese de Doctorat d'Etat en Math\'ematiques, Universit\'e Paris VI, 25 June 1981.

Petitjean M. Agr\'egation des similarit\'es: une solution oubli\'ee. RAIRO Oper. Res. 2002,36[1],101-108.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 ## pop from a data matrix data <- matrix(c(1,1,1,1,1 ,1,2,1,2,1 ,2,3,2,3,2 ,2,4,3,3,2 ,1,2,4,2,1 ,2,3,2,3,1),ncol=5,byrow=TRUE) pop(diss(data)) ## pop from a dissimilarity matrix d <-2 * matrix(c(9, 8, 5, 7, 7, 2 , 8, 9, 2, 5, 1, 7 , 5, 2, 9, 8, 7, 1 , 7, 5, 8, 9, 3, 2 , 7, 1, 7, 3, 9, 6 , 2, 7, 1, 2, 6, 9),ncol=6,byrow=TRUE) - 9 pop(d) ## Not run: d <- 2 * matrix(c(57, 15, 11, 32, 1, 34, 4, 6, 17, 7 , 15, 57, 27, 35, 27, 27, 20, 24, 30, 15 , 11, 27, 57, 25, 25, 20, 34, 25, 17, 15 , 32, 35, 25, 57, 22, 44, 13, 22, 30, 11 , 1, 27, 25, 22, 57, 21, 28, 43, 20, 13 , 34, 27, 20, 44, 21, 57, 18, 27, 21, 8 , 4, 20, 34, 13, 28, 18, 57, 31, 28, 13 , 6, 24, 25, 22, 43, 27, 31, 57, 30, 15 , 17, 30, 17, 30, 20, 21, 28, 30, 57, 12 , 7, 15, 15, 11, 13, 8, 13, 15, 12, 57),ncol=10,byrow=TRUE) - 57 pop(d) ## End(Not run)

amap documentation built on May 30, 2017, 7:36 a.m.