SwissMunicipalities

The SwissMunicipalities dataset provided in SamplingStrata refers to the Swiss municipalities in 2003.
Each municipality belongs to one of seven regions which are equivalent to provinces.
Each region contains a number of cantons, which are administrative subdivisions.
There are 26 cantons in Switzerland.
The data contains 2,896 observations (each observation refers to a Swiss municipality in 2003).

The target variables are:

POPTOT (total population) Hapoly (municipality area)

TThe auxiliary variables are:

Surfacebois (wood area) Airbat (area with buildings)

library(SamplingStrata)
data("swissmunicipalities")
df <- swissmunicipalities[,c(1,3,6:8,23)]
# df$DOM <- 1
df$HApoly.cat <- var.bin(df$HApoly,15)
df$POPTOT.cat <- var.bin(df$POPTOT,15)
frame <- buildFrameDF(df=df,
                      id="id",
                      X=c("HApoly.cat","POPTOT.cat"),
                      Y=c("Surfacesbois",
                          "Surfacescult"),
                      domainvalue = "REG")
strata <- buildStrataDF(frame,progress=FALSE, verbose=FALSE)
ndom <- length(unique(frame$domainvalue))
cv <- as.data.frame(list(DOM=rep("DOM1",ndom),
                         CV1=rep(0.05,ndom),
                         CV2=rep(0.05,ndom),
                         domainvalue=c(1:ndom)))
cv

##    DOM  CV1  CV2 domainvalue
## 1 DOM1 0.05 0.05           1
## 2 DOM1 0.05 0.05           2
## 3 DOM1 0.05 0.05           3
## 4 DOM1 0.05 0.05           4
## 5 DOM1 0.05 0.05           5
## 6 DOM1 0.05 0.05           6
## 7 DOM1 0.05 0.05           7

library(hEDA)
dom<-unique(strata$DOM1)
ndom<-length(unique(strata$DOM1))
Kmean<-SamplingStrata::KmeansSolution(strata,
                      errors=cv,
                           nstrata=NA,
                           minnumstrat=2,
                           maxclusters = 20,
                           showPlot=FALSE)

## 
## -------------------
##   Kmeans solution 
## -------------------
##  *** Domain:  1  ***
##  Number of strata:  20
##  Sample size     :  78
##  *** Domain:  2  ***
##  Number of strata:  14
##  Sample size     :  58
##  *** Domain:  3  ***
##  Number of strata:  11
##  Sample size     :  35
##  *** Domain:  4  ***
##  Number of strata:  8
##  Sample size     :  26
##  *** Domain:  5  ***
##  Number of strata:  20
##  Sample size     :  66
##  *** Domain:  6  ***
##  Number of strata:  12
##  Sample size     :  35
##  *** Domain:  7  ***
##  Number of strata:  20
##  Sample size     :  91
## -------------------
##  Total size:  389
## -------------------

nstrata<-NULL
for(i in 1:ndom){
nstrata[i]<-length(table(Kmean$suggestions[which(Kmean$domainvalue==dom[i])]))
}
nstrata

## [1] 20 14 11  8 20 12 20

Kmeans solution

##The kmeans algorithm: - separates observations in to \(k\)-clusters (where \(k\) is an input parameter), so that the within sum of squares in each cluster is minimised - The K-meansSolution algorithm generates clusterings for iteratively increasing values of k - The goal is to select a good quality solution: that solution will have k clusters. - The algorithm starts by partitioning the atomic strata into the k clusters (strata), where k starts at a value, e.g. 2, based on their proximity to a stratum centroid (the mean of each of the relevant values from the target variable columns in that stratum). This solution is then evaluated using the Bethel-Chromy algorithm. - The K-means algorithm then proceeds to create a solution with k + 1 strata and again evaluates that solution. This process is repeated for each k up to a predefined maximum number of strata (in this example 20 strata). The solution which provides the lowest cost is then chosen to be the initial solution. -

Kmean<-SamplingStrata::KmeansSolution(strata,
                      errors=cv,
                           nstrata=NA,
                           minnumstrat=2,
                           maxclusters = 20,
                           showPlot=FALSE)
nstrata<-NULL
for(i in 1:ndom){
nstrata[i]<-length(table(Kmean$suggestions[which(Kmean$domainvalue==dom[i])]))
}
nstrata

Kmedoids (pam) solution

Source: https://link.springer.com/referenceworkentry/10.1007/978-0-387-30164-8_426 - The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. - - K-medoids clustering is a variant of K-means that is more robust to noises and outliers. - Instead of using the mean point as the center of a cluster, K-medoids uses an actual point in the cluster to represent it. - Medoid is the most centrally located object of the cluster, with minimum sum of distances to other points. - As an alternative to pam consider using the median - Mean is greatly influenced by the outlier and thus cannot represent the correct cluster center, while medoid is robust to the outlier and correctly represents the cluster center.

library(hEDA)
library(cluster)
Kmedoids<-kmedoidsSolution(strata,
                      errors=cv,
                           nstrata=NA,
                           minnumstrat=2,
                           maxclusters = 20,
                           showPlot=FALSE)
nstrata<-NULL
for(i in 1:ndom){
Kmedoidsnstrata[i]<-length(table(Kmedoids$suggestions[which(Kmedoids$domainvalue==dom[i])]))
}
Kmedoidsnstrata

##    DOM  CV1  CV2 domainvalue
## 1 DOM1 0.05 0.05           1
## 2 DOM1 0.05 0.05           2
## 3 DOM1 0.05 0.05           3
## 4 DOM1 0.05 0.05           4
## 5 DOM1 0.05 0.05           5
## 6 DOM1 0.05 0.05           6
## 7 DOM1 0.05 0.05           7

## 
## -----------------
## 
##  Kmedoids solution 
## -----------------
##  *** Domain:  1  ***
##  Number of strata:  20
##  Sample size     :  81
##  *** Domain:  2  ***
##  Number of strata:  16
##  Sample size     :  62
##  *** Domain:  3  ***
##  Number of strata:  12
##  Sample size     :  39
##  *** Domain:  4  ***
##  Number of strata:  14
##  Sample size     :  29
##  *** Domain:  5  ***
##  Number of strata:  20
##  Sample size     :  68
##  *** Domain:  6  ***
##  Number of strata:  15
##  Sample size     :  33
##  *** Domain:  7  ***
##  Number of strata:  20
##  Sample size     :  94
##  Total Sample size     :  406

## [1] 20 16 12 14 20 15 20

Fuzzy k-means (or c-means) clustering solution

In K-means clustering a sample unit belongs to one cluster only, whereas in fuzzy K-means it belongs to all clusters (or strata) at varying probabilities.
In the fuzzy K-means algorithm (also called the fuzzy c-means algorithm (Bezdek et al., 1984) a) each centroid of a cluster (\(c_{k}\)) is the mean of all cases (basic strata) in the data set, weighted by their degree of belonging to that cluster (\(w_{k}\)).
Like K-means the algorithm is affected by the initial clusters and tends to find local minima (Morissette and Chartier, 2013)
Nonetheless, the algorithm is useful in scenarios where there is some degree of overlap between clusters and it would be useful to consider to what degree an sample unit belongs to various clusters.
The soft clustering helps to smooth out the likelihood of the algorithm finding inferior quality local minima (Klawonn, 2004)

library(hEDA)
fuzzy<-fuzzySolution(strata,
                    cv,
                    minClusters=2,
                    maxclusters = 20)
#sample size
sum(fuzzy[[2]])
#adapt so it can be used in hEDA
fuzzySol<-Kmean
for(i in 1:ndom){
  fuzzySol$suggestions[which(fuzzySol$domainvalue==dom[i])]<-unlist(fuzzy[[1]][i])
}

## [1] 346.9717

Hyperparameters

Hyperparameters are set manually or automatedly in advance of the algorithm, and they refer to controllable parameters such as the number of iterations the algorithm runs for, the number of solutions evaluated in a given iteration (e.g. grouping genetic algorithm), or the rate at which the probability of accepting inferior solutions decays (e.g. simulated annealing algorithm).
These hyperparameters need to be sufficient for the algorithm to be effective in finding an optimal or near optimal solution. However, they are generally specific to the characteristics of the input data (i.e. dimensions of basic strata, the degree of their homogeneity, and number of domains), and whether algorithm is exploratory, exploitative, or a combination of both, in nature.
The hyperparameters used in the experiments for the HEDA are provided below: Temp=0.0001; rate of accepting inferior solutions decrement_constant=0.95; decay of Temperature jsize=5; Number of Sequences length_of_markov_chain =50; length of sequence SAAiters=50; Number of iterations at which the SAA is run popSize = 20; Population of solutions size iters = 200; Number of hEDA solutions mutationChance = 0.01; Mutation chance elitism = 0.1; Elitism rate EDAfreq=1; frequency of EDA kmax_percent=0.025; rate of accepting large perturbations in first sequence ProbNewStratum=0.0001; Probability of creating new stratum

Testing HEDA

ptm <- proc.time()
outpar<-hEDA::parallelhEDA(strata, cv, fuzzySol,
                     Temp=0.0001,initialStrata=nstrata, decrement_constant=0.95, end_time =Inf,
                     jsize=5,length_of_markov_chain =50,
                     SAArun=TRUE,SAAiters=50,
                     popSize = 20, iters = 50, mutationChance = 0.01, elitism = 0.1,
                     addStrataFactor=0.000001, EDAfreq=1,
                     verbose = FALSE, dominio=dom,minnumstrat=2,kmax_percent=0.025,ProbNewStratum=0.0001,
                     strcens=FALSE,writeFiles=FALSE, showPlot=TRUE, minTemp = 0.000005, realAllocation=TRUE)
Time<-proc.time() - ptm
sum(unlist(outpar$SampleSize))
Time

Comparing with GGA

ptm <- proc.time()
solutionGGA <-optimizeStrata(errors=cv, strata, cens = NULL, strcens = FALSE, alldomains = TRUE,

                            dom = NULL, initialStrata = nstrata, addStrataFactor = 0, minnumstr = 2,

                            iter = 400, pops = 20, mut_chance = NA, elitism_rate = 0.2,

                            highvalue = 1e+08, suggestions =fuzzySol,
                            realAllocation = TRUE,

                            writeFiles = TRUE, showPlot = TRUE, parallel = TRUE, cores = NA)

Time<-proc.time() - ptm
Time

Bibliography

Bezdek, J. C. (1981). Objective function clustering. In Pattern recognition with fuzzy objective function algorithms, pp. 43–93. Springer.

Klawonn, F. (2004). Fuzzy clustering: Insights and a new approach. Mathware & soft computing. 2004 Vol. 11 Núm. 3.

Morissette, L. and S. Chartier (2013). The k-means clustering technique: General considerations and implementation in mathematica. Tutorials in Quantitative Methods for Psychology 9(1), 15–24.

hEDA part3

Mervyn OLuing

12/7/2022