Anthropometry (version 1.1)

trimowa: Trimmed PAM with OWA operators

Description

This is the methodology developed in Ibanez et al. (2012) to define an efficient apparel sizing system based on clustering techniques jointly with OWA operators. In our approach, we apply the trimmed k-medoids algorithm (trimmedoid) to the first twelve bust classes according to the sizes defined in the European standard on sizing systems. Size designation of clothes. Part 3: Measurements and intervals.

Usage

trimowa(x,w,K,alpha,niter,Ksteps,ahVect=c(23,28,20,25,25))

Arguments

x

Data frame. In our approach, this is each of the subframes originated after segmenting the whole anthropometric Spanish survey into twelve bust segments, according to the European standard on sizing systems. Size designation of clothes. Part 3: Measuremen

w

The aggregation weights of the OWA operators. They are computed with the WeightsMixtureUB.

K

Number of clusters.

alpha

Proportion of trimmed sample.

niter

Number of random initializations.

Ksteps

Steps per initialization.

ahVect

Constants that define the ah slopes of the distance function in GetDistMatrix. Given the five variables considered, this vector is c(23,28,20,25,25). This vector would be different accord

Value

• A list with the following elements:

  meds: Centroids of the clusters. They are the medoids obtained for each bust class.

  numTrim: Number of trimmed individuals in each bust class.

  numClass: Number of individuals in each bust class.

  noTrim: Number of of non-trimmed individuals.

  C1,C2,C3,C4: Required constant values to define the distance GetDistMatrix (C1 is bh, C2 is bl, C3 is ah and C4 is al).

  asig: Vector of the clusters to which each individual belongs.

  trimm: Trimmed individuals.

References

Ibanez, M. V., Vinue, G., Alemany, S., Simo, A., Epifanio, I., Domingo, J., and Ayala, G., (2012). Apparel sizing using trimmed PAM and OWA operators, Expert Systems with Applications 39, 10512--10520.

European Committee for Standardization. Size designation of clothes. Part 3: Measurements and intervals. (2005).

See Also

dataDemo, WeightsMixtureUB, GetDistMatrix, trimmedoid

Examples

#Loading the data to apply the trimowa algorithm:
dataDef <- dataDemo
num.variables <- dim(dataDef)[2]
bust <- dataDef$bust

orness <- 0.7
w <- WeightsMixtureUB(orness,num.variables)

bustCirc_4 <- seq(74,102,4)  ; bustCirc_6 <- seq(107,131,6)  ; bustCirc <- c(bustCirc_4,bustCirc_6) 
nsizes <- length(bustCirc)
K <- 3 ; alpha <- 0.01 ; niter <- 10 ; Ksteps <- 7

ahVect <- c(23,28,20,25,25)

res_trimowa <- list()
for (i in 1 : (nsizes-1)){ 
  data = dataDef[(bust >= bustCirc[i]) & (bust < bustCirc[i + 1]), ]   
  res_trimowa[[i]] <- trimowa(data,w,K,alpha,niter,Ksteps,ahVect=ahVect)
}